Back to SBR-index Short Book Reviews Reviews 2001

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Title OPTIMIZATION: FOUNDATIONS AND APPLICATIONS. Author R.E. Miller. Publisher New York: Wiley, 2000, pp. xvii + 653, £58.50.

Contents:

PART I: Foundations: Linear Methods

1. Matrix algebra

2. Systems of linear equations

PART II: Foundations: Nonlinear Methods

3. Unconstrained maximization and minimization

4. Constrained maximization and minimization

PART III: Applications: Iterative Methods for Nonlinear Problems

5. Solving nonlinear equations

6. Solving unconstrained maximization and minimization problems

PART IV: Applications: Constrained Optimization in Linear Models

7. Linear programming: Fundamentals

8. Linear programming: Extensions

9. Linear programming: Interior point methods

PART V: Applications: Constrained Optimization in Nonlinear Models

10. Nonlinear programming: Fundamentals

11. Nonlinear programming: Duality and computational methods Readership: Operational researchers, mathematical programmers A more appropriate title for this text would be "Optimization: Foundations and Algorithms"; applications only occur in the exercises. This is a modern book in that it covers linear and nonlinear programming and so is able to include a valuable section on Interior Point Methods. The material is presented in an informal fashion using, where possible, geometric interpretations to support the algebra. The algorithms are described in a well-blended mixture of algebraic and numerical examples. The author is concerned about neither mathematical proofs of convergence nor a practitioner's interest in when convergence will happen. The author's extensive teaching experience is reflected in his upbeat, relaxed writing style and presentation. Should you have to teach undergraduate optimization to non-mathematicians this book would be useful. At the end of each chapter there are references and problems. It is a pity that many of the recent survey books and articles are not included in the references.

Reviewer: Institute London School of Economics Place London, U.K. Name S. Powell

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Title PUBLIC POLICY AND STATISTICS: CASE STUDIES FROM RAND. Author S.C. Morton and J.E. Rolph (Eds.). Publisher New York: Springer-Verlag, 2000, pp. xii + 243, US$49.95/DM98.00/£33.48.-T

Contents:

PART I: Collecting Data

1. School-based drug prevention: Challenges in designing and analyzing social experiments

2. The health insurance experiment: Design using the finite selection model

3. Counting the homeless: Sampling difficult populations

PART II: Defecting Effects

4. Periodicity in the global mean temperature series?

5. Racial bias in death sentencing: Assessing the statistical evidence

6. Malpractice and the impaired physician: An application of matching

PART III: Understanding Relationships

7. Supply delays for F-14 jet engine repair parts: Developing and applying effective data graphics

8. Hospital mortality rates: Comparing with adjustments for case mix and sample size

9. Eye-care supply and need: Confronting uncertainty

10. Modeling block grant formulas for substance abuse treatment Readership: Advanced undergraduate and graduate students of statistics and/or public policy, and empirical researchers and policy makers (especially at government and other research institutes) RAND is a research institute created by the U.S. Air Force originally with a mandate "to provide objective research on national security issues." It is now an independent research organization that through grants and contracts from a variety of sources provides a research resource for public policy makers. The RAND Statistics Group was formed in 1976 and this book is a collection of some of their case studies.

Authored by the statistical investigators, each chapter lays out a statistical case study in a common nine section format: Executive Summary, 1. Introduction (always comprised of A. Policy Problem, B. Research Questions, C. Statistical Questions, and D. Summary of Data and Methods), 2. Design, Data Collection, Description of Data Sources and Description of Data, 3. Datafile Creation, Destructive Stats and Exploratory Analysis, 4. Statistical Methods and Models, 5. Results, 6. Discussion (covering policy implications and statistical issues), 7. Exercises and finally further RAND Reading (accessible at www.rand.org ). The sets of data for each chapter and errata are available on the Web (www.rand.org/centers/stat/casebook).

As with any collection of papers, some chapters are better than others; as with any statistical investigation, different approaches might have been taken in each case. Rather than detract from the book, these make the book a more interesting resource to be enlivened by an instructor of an advanced undergraduate or graduate course in statistics. Students of public policy might find the statistical aspects of the case studies somewhat challenging.

I strongly recommend it as a resource to instructors in statistics. Its breadth of applications and its organization of topics within papers make the book an important contribution to the growing collection of books on case studies in statistics.

Reviewer: Institute University of Waterloo Place Waterloo, Canada Name R.W. Oldford

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Title THE PURSUIT OF PERFECT PACKING. Author T. Aste and D. Weaire. Publisher Bristol, Philidelphia: Institute of Physics, 2000, pp. xi + 136, £45.00/US$45.00 Cloth; £17.50/US$29.00 Paper.

Contents:

1. How many sweets in the jar?

2. Loose change and tight packing

3. Hard problems with hard spheres

4. Proof positive?

5. Peas and pips

6. Enthusiastic admiration: The honeycomb

7. Toils and troubles with bubbles

8. The architecture of the world of atoms

9. Apollonius and concrete

10. The giant's causeway

11. Soccer balls, golf balls and buckyballs

12. Packing and kisses in high dimensions

13. Odds and ends

14. Conclusion Readership: An entertaining introduction to the field for both specialists and the more general public This book is packed with examples of 'packing' in mathematics, physics, biology and engineering. In 1998 a solution was claimed (by Thomas Hales) to the long-standing Kepler conjecture – that no arrangement of spheres of equal radius in three-dimensional space has density greater than that of the face-centred cubic packing.

This remarkable result would provide a resolution, where many previous attempts have been found wanting. The Kepler conjecture is, in fact, a particular part of the eighteenth of the famous twenty-three problems posed by David Hilbert in 1900 to guide mathematical research.

The style of this book is concise and informal, but the material which is included, together with key references, will enable the curious reader to follow up the conjecture in its historical context and a large number of related problems with extensive applications. This is an excellent read!

Reviewer: Institute Imperial College of Science, Technology and Medicine Place London, U.K. Name F.H. Berkshire

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Title Rutherford. Scientist Supreme. Author J. Campbell, Foreword by M. Oliphant. Publisher Christchurch, New Zealand: AAS Publications, pp. xv + 515, US$40.00.

Contents:

1. Lucky infant – carefree child

2. Tragedies and triumphs

3. Earnest schoolboy

4. Academia

5. Senior undergraduate

6. Apprenticeship in research

7. Planning the future

8. Wireless signalling

9. The new physics

10. Natural alchemy

11. Consolidating a Nobel prize

12. The Nobel prize

13. Counting atoms

14. The atom unveiled

15. The world at war

16. Broadening research

17. Triumphal tour of home

18. Death and glory

19. Birth of the atom smashers

20. Elder statesman of science

21. Sundown Readership: General This book provides a comprehensive, scholarly and eminently readable account of the life of Ernest (Lord) Rutherford. He was born at Brightwater, New Zealand. The author traces Rutherford's school years at Foxhill, Havelock and Nelson College. From here he won a scholarship to Canterbury College, Christchurch, where he earned his first degree in 1892. Inspired by a colourful and controversial Professor Bickerton, he commenced research on a subject of his own choosing, the magnetization of iron at higher frequencies.

In 1895 he won an 1851 Scholarship with which he chose to work with Professor J.J. Thomson at the Cavendish Laboratory, Cambridge, England. There he began, work on the long distance detection of Hertzian waves and by 1896 had established a world distance record. At this point Thomson invited him to assist him in trying to understand electrical conduction in gases. When radioactivity was discovered a short time later, Rutherford utilized radioactivity which became his life's work.

Rutherford's appointment as Professor of Physics at McGill University, Montreal, brought him to Canada in 1898, and he continued his researches there until 1907. This book provides the first full study of his life and his work whilst in North America. His work was so successful that he was nominated for the Nobel prize in both Physics and Chemistry. He was awarded a Nobel prize in Chemistry in 1908 for demonstrating that radioactivity involves the natural transmutation of one atom species into another.

By this time Rutherford was Professor of Physics at the University of Manchester, England. He continued work on the scattering of á-particles and soon found that a very small number were scattered backwards. This led to the formulation of the nuclear model of the atom, a fundamental advance which should have justified the award of a Nobel prize in Physics. But in view of his earlier award, a second award was deemed unnecessary.

In 1919 he was appointed Cavendish Professor of Physics at Cambridge, succeeding Professor J.J. Thomson. The Cavendish Laboratory became the world centre for Physics and, under Rutherford's leadership, attracted such luminaries as Appleton, Blackett, Chadwick, Cockroft, Kapitza and Walton, all of whom won Nobel prizes. Rutherford died unfortunately in 1937 of a strangulated hernia. Being a Lord, protocol required that he be operated on by a titled doctor. The delay cost him his life.

This is a delightful book, full of anecdotes and quotations from original letters and documents. As one reads the book one can feel the state of development in science at every stage. It can be enjoyed by anyone, from high school student to research scientist.

Reviewer: Institute National Research Council of Canada Place Ottawa, Canada Name D.A. Ramsay

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Title An Introduction To Cryptography. Author R.A. Mollin. Publisher Boca Raton, Florida: Chapman and Hall/CRC Press, 2001, pp. xiii + 373, US$79.95/£29.95.

Contents:

1. Origins, computer arithmetic and complexity

2. Symmetric-key cryptosystems

3. Public-key cryptosystems

4. Primality testing

5. Factoring

6. Advanced topics Readership: Cryptography buffs from beginning undergraduate to research scientist This is a great book! It can be used in many ways: for a university course at one extreme, and as selective light reading for pleasure at the other. The author's enthusiasm carries the reader along clearly and easily, spilling over to scores of fascinating, beautifully written footnotes, which include more than fifty mini-biographies. There are close to three hundred problems, half with solutions. The other half are available only in a solutions manual for the instructor; this, however, is a decision I deplore. Apart from that, this book is excellent and highly recommended.

Reviewer: Institute University of Wisconsin Place Madison, U.S.A. Name N.R. Draper

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Title Statistics for Environmental Science and Management. Author B.F.J. Manly. Publisher Boca Raton, Florida: Chapman and Hall/CRC, 2001, pp. ix + 326, US$49.95/£24.99.

Contents:

1. The role of statistics in environmental science

2. Environmental sampling

3. Models for data

4. Drawing conclusions from data

5. Environmental monitoring

6. Impact assessment

7. Assessing site reclamation

8. Time series analysis

9. Spatial data analysis

10. Censored data

11. Monte Carlo risk assessment

12. Final remarks

APPENDIX A: Some Basic Statistical Methods

APPENDIX B: Statistical Tables Readership: Statisticians, environmental scientists and managers, ecologists This book is directed at environmentalists and those who have to make difficult and sometimes costly decisions about the environment. Such decisions should be based upon pertinent information, and much of that is statistical. This book aims to introduce the reader to statistical methods that are useful for this. Many of the methods described are standard, such as analysis of variance, multiple regression, simply repeated measures designs and time series. Other more specialized methods particularly suitable for this area, such as meta analysis, kriging for spatial data, point processes and censored data methods, are introduced. Although many numerical examples are given, the emphasis is upon creating an awareness of what methods are available rather than the acquisition of technical skill. Further information can be obtained from the extensive reference section.

Now that complex statistical models can be fitted with ease, challenges of appropriate data collection and interpretation still remain. This is especially true with environmental data which are mainly observational rather than experimental. Stress is laid on the importance of defining the sampling unit on the difference between true and pseudo-replication and on the formulation of appropriate null hypotheses. These topics, which have been the subject of much debate in the medicinal, pharmaceutical and ecological literature, receive scant attention in the mainstream statistical literature. The author pays special attention to the distinction between design-based inference where randomness is introduced by the manner in which the data are collected, and model-based inference where randomness is introduced by the model assumptions. On the whole he favours design-based inference and randomization tests of hypotheses.

This book will do much to promote good statistical practice in environmental matters, an area of worldwide concern.

Reviewer: Institute University of Cape Town Place Rondebosch, South Africa Name J.M. Juritz

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Title Applied Nonparametric Statistical Methods, 3rd edition. Author P. Sprent and N.C. Smeeton. Publisher Boca Raton, Florida: Chapman and Hall/CRC, 2001, pp. ix + 461, US$59.95/£29.99.[Original 1989; Short Book Reviews, Vol. 9, p. 47]

Contents:

1. Introducing nonparametric methods

2. Centrality inference or single samples

3. Other single-sample inference

4. Methods for paired samples

5. Methods for two independent samples

6. Three or more samples

7. Correlation and concordance

8. Regression

9. Categorical data

10. Association in categorical data

11. Robust estimation Readership: Statisticians, students of statistics, research workers, consultants This is the third edition of a very good book on nonparametric statistics. It is a book for the practitioner, but it goes far beyond being a compendium of useful methods. It aims at promoting understanding as well. Good statistical practice is exemplified throughout. The results of each procedure are evaluated in terms of the power of the test and also interpreted in the context of the test study. Theoretical derivations are avoided, but the authors give insight by discussing simple numerical examples. Particularly commendable are their discussions of multiple comparisons and conditioning in the two by two contingency table.

Hypothesis testing has a central role in nonparametric inference. Improved software, StatXact in particular, has made exact p-values readily available. The authors use the p-value as a tool for weighing evidence against the null hypothesis rather than as the decision tool implicit in a fixed level of significance. This approach answers much of the criticism associated with over enthusiastic use of p-values. Attention too is given to interval estimation, which can be a difficult problem with nonparametric inference. In many cases, comparisons with asymptotic results and corresponding parametric methods are given.

Topics in the third edition include discussions of robust estimation, the bootstrap, angular data, capture recapture methods and the measurement of agreement between observers.

If I could only have one book on nonparametric methods, this would be my choice. It is highly recommended.

Reviewer: Institute University of Cape Town Place Rondebosch, South Africa Name J.M. Juritz

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Title Fundamentals of Modern Statistical Methods. Author R.R. Wilcox. Publisher New York: Springer-Verlag, 2001, pp. xiii + 258.

Contents:

1. Introduction

PART I

2. Getting started

3. The normal curve and outlier detection

4. Accuracy and inference

5. Hypothesis testing and small sample sizes

6. The bootstrap

7. A fundamental problem

PART II

8. Robust measures of location

9. Inferences about measures of location

10. Measures of association

11. Robust regression

12. Alternative strategies Readership: Teachers in statistics, researchers and applied statisticians This interesting book gives a very readable introduction about understanding basic statistics from the point of view of modern developments and insights achieved during the last forty years. The book has two parts. In Part I, which covers basic concepts, the aim is to provide a verbal and graphical explanation of why standard methods can be highly misleading and provides a framework for intuitively understanding the practical advantages of modern techniques. In Part II, the goal is to explain basic modern methods for dealing with the problems described in Part I to applied researchers.

This is an excellent book, which gives a thorough and very clear description of today's most important techniques of the basic standard methods with some historical background and special attention to keep the technical details to a minimum. The author has performed a real service to the profession. I enjoyed reading this book. However, I found a few minor typographical errors (e.g. pp. 56, 164), and there is a lack of complete references. The book is not only highly recommended, but it should be required reading for anyone embarking on a career as a statistician in any field where a critical evaluation of data is required.

Reviewer: Institute Isfahan University of Technology Place Isfahan, Iran Name A. Parsian

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Title Probability Essentials. Author J. Jacod and P. Protter. Publisher Berlin: Springer-Verlag, 2000, pp. x + 250, US$36.00.

Contents:

1. Introduction

2. Axioms of probability

3. Conditional probability and independence

4. Probabilities on a countable space

5. Random variables on a countable space

6. Construction of a probability measure

7. Construction of a probability measure on R

8. Random variables

9. Integration with respect to a probability measure

10. Independent random variables

11. Probability distributions on R

12. Probability distributions on Rπ

13. Characteristic functions

14. Properties of characteristic functions

15. Sums of independent random variables

16. Gaussian random variables (The normal and the multivariate normal distributions)

17. Convergence of random variables

18. Weak convergence

19. Weak convergence and characteristic functions

20. The laws of large numbers

21. The central limit theorem

22. L2 and Hilbert spaces

23. Conditional expectation

24. Martingales

25. Supermartingales and submartingales

26. Martingale inequalities

27. Martingale convergence theorems

28. The Randon-Nikodym theorem Readership: Students at the graduate level needing a streamlined introduction to probability theory The authors provide the shortest path through the twenty-eight chapter headings. The topics are treated in a mathematically sound and pedagogically digestible way. The writing is concise and crisp: the average chapter length is about eight pages. The topics treated are those one would expect, except perhaps for the proof of Kolmogorov's strong law using backward martingales and a version of the martingale central limit theorem. Numerous exercises add to the value of the text as a teaching tool. In conclusion, this is an excellent text for the intended audience

Reviewer: Institute ETH-Zürich Place Zürich, Switzerland Name P.A.L. Embrechts

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Title Combinatorial Methods in Density Estimation. Author L. Devroye and G. Lugosi. Publisher New York: Springer-Verlag, 2000, pp. xii + 208, US$44.95/DM92.00.

Contents:

1. Introduction

2. Concentration inequalities

3. Uniform deviation inequalities

4. Combinatorial tools

5. Total variation

6. Choosing a density estimate

7. Skeleton estimates

8. The minimum distance estimate: Examples

9. The kernel density estimate

10. Additive estimates and data splitting

11. Bandwidth selection for kernel estimates

12. Multiparameter kernel estimates

13. Wavelet estimates

14. The transformed kernel estimate

15. Minimax theory

16. Choosing the kernel order

17. Bandwidth choice with superkernels Readership: Graduate students and researchers in statistics This book is built around a new look on the important problem of bandwidth selection in density estimation. This new method has been launched in two recent papers of the two authors in the Annals of Statistics. It is based on ideas of minimum distance methods and convergence theory for empirical measures, uniformly over certain classes. The method aims at finding estimators with universal properties that is valid for all (or nearly all) densities. The book is self-contained because a lot of fundamental inequalities and essential combinatorial techniques are collected in the first part of the book. There is a rich choice of exercises, some of which may be quite hard. This makes it interesting for classroom teaching. It is an attractive book that certainly provides inspiration for further research.

Reviewer: Institute Limburgs Universitair Centrum Place Diepenbeek, Belgium Name N.D.C. Veraverbeke

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Title AN INVARIANT APPROACH TO STATISTICAL ANALYSIS OF SHAPES. Author R.S. Lele and J.T. Richtsmeier. Publisher Boca Raton, Florida: Chapman and Hall/CRC, 2001, pp. viii + 308. £46.99.

Contents:

1. Introduction

2. Morphometric data

3. Statistical models for landmark coordinate data

4. Statistical methods for comparisons of forms

5. The study of growth

6. Classification and clustering algorithms

7. Further applications of EDMA Readership: Statisticians, biologists, medical researchers, anthropologists The shape of a multivariate set of data is formally defined as a statistic that is maximally invariant under Euclidean motions and homotheties. Reflections are optional. Recent statistical work in the theory of random shapes has shed some light on new techniques for morphometrics using landmark based methods. In general terms, morphometrics can be defined as the quantitative study of shape and form. The idea that both shape and form statistics are maximal invariants has been around since the 1970s. Its incorporation into the morphometric literature, with its roots in D'Arcy Thompson's pioneering work on the growth and form, has been more recent.

The appearance of this book by Subhash Lele and Joan Richtsmeier is to be welcomed. In recent years there has been much discussion of the relative advantages of morphometric methodology developed by Fred Bookstein and his colleagues versus the EDMA approach advocated by Lele and Richtsmeier. Now readers can decide for themselves.

Reviewer: Institute University of Waterloo Place Waterloo, Canada Name C.G. Small

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Title Fitting Statistical Distributions. Author Z.A. Karian and E.J. Dudewicz. Publisher Boca Raton, Florida: CRC Press, 2000, pp. xvii + 438, US$53.99.

Contents:

1. The generalized lambda family of distributions

2. Fitting distributions and data with the GLD via the method of moments

3. The extended GLD system, the EGLD: fitting by the method of moments

4. A percentile based approach to fitting distributions and data with the GLD

5. GLD-2: the bivariate GLD distributions

6. The generalized bootstrap (GB) and Monte Carlo (MC) methods

Appendix A: Programs for Fitting GLD, GBD and GLD-2

Appendix B: Tables for GLD Fits

Appendix C: Tables for GBD Fits

Appendix D: Tables for GLD-2 Fits

Appendix E: Normal Distribution Readership: Anyone who wants to fit a parameterized family to a distribution or to data The GLD is a four-parameter distribution with a lot of flexibility. It can be fitted to a lot of distributions and a lot of sets of data. The basic method has various extensions and the authors are experts in the area. The presentation is careful and extensive but somewhat remorseless. The main text is 283 pages and these are followed by 154 pages of programs, tables, references and index. Each reference contains its page reference listings, a nice feature. This is a specialized book and it will be welcomed by the appropriate specialists.

Reviewer: Institute University of Wisconsin Place Madison, U.S.A. Name N.R. Draper

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Title Probability and Statistical Inference. Author N. Mukhopadhyay. Publisher New York: Dekker, 2000, pp. xviii + 665, US$95.00/£60.40.

Contents:

1. Notions of probability

2. Expectations of functions of random variables

3. Multivariate random variables

4. Functions of random variables and sampling distribution

5. Concepts of stochastic convergence

6. Sufficiency, completeness, and ancillarity

7. Point estimation

8. Tests of hypotheses

9. Confidence interval estimation

10. Bayesian methods

11. Likelihood ratio and other tests

12. Large-sample inference

13. Sample size determination: Two-stage procedures Readership: Statistics or mathematics/statistics majors; first year graduate students in statistics or areas requiring

substantial understanding A striking feature of this volume is the very large number of worked examples in the text, together with long sets of exercises and complements at the end of each chapter. This is an introductory text; no previous knowledge of probability or statistical theory is assumed, but a reasonably confident approach to mathematics seems desirable. The stated prerequisite is a year of calculus; the author considers this to be enough to understand a major portion of the book, but admits that "some familiarity with linear algebra, multiple integration and partial differentiation will be beneficial" for some sections of the book. Measure theory is neither required nor invoked. Mathematically, the general approach is reasonably rigorous; full proofs are given for a number of important results. In a pleasantly informal style, the author provides very helpful explanations and comments on the mathematical results throughout the book. This tutorial structure makes the book an excellent choice for self-study. In addition to a list of abbreviations and notation, and a few standard tables, the Appendix contains an unusual feature – brief biographical sketches of eighteen leading statisticians (including Fisher, Cramér, Kolmogorov, Neyman, Pearson, C.R. Rao, …). Many historical comments enliven the main text as well. There is a very useful bibliography of nearly three hundred entries. All this helps to make the book a handy reference as well as a good textbook.

Reviewer: Institute University of St. Andrews Place St. Andrews, U.K. Name C.D. Kemp

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Title Practical Time series. Author G. Janacek. Publisher London: Arnold, New York: Oxford University Press, 2001, pp. xv + 156, £17.99.

Contents:

1. Introduction

2. Exponential smoothing

3. Stationary series

4. The state space approach

5. Fitting ARIMA models

6. The frequency domain and the spectrum

7. Estimation and use of power spectrum

8. Two or more series

9. The R language Readership: Statisticians, management scientists The purpose of the book is to provide standard time series tools to a non-specialist with a reasonable background in mathematical statistics. The whole approach in the book is very informal, and the author took great care in explaining all necessary techniques in non-specialist terms without making it too technical. The standard topics, like identification, estimation and prediction associated with standard linear time series models, are covered. The author has provided time series routines in R which are freely available, and a website with sets of data from which any reader can download is also given. I found the book interesting and easy to understand. I believe the book will be useful to many who are interested in actual applications of time series and also to undergraduate and graduate students. I strongly recommend this book.

Reviewer: Institute University of Manchester Science and Technology Place Manchester, U.K. Name T. Subba Rao

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Title A Course in Time Series Analysis. Author D. Peña, G.C. Tiao and R.S. Tsay (Eds.). Publisher New York: Wiley, 2001, pp. xvii + 460, £82.50.

Content:

1. Introduction by D. Peña and G.C. Tiao

PART I: Basic Concepts in Univariate Time Series

2. Univariate time series: Autocorrelation, linear prediction, spectrum, and state-space model by G.T. Wilson

3. Univariate autoregressive moving-average models by G.C. Tiao

4. Model fitting and checking, and the Kalman Filter by G.T. Wilson

5. Prediction and model selection by D. Peña

6. Outliers, influential observations, and missing data by D. Peña

7. Automatic modelling methods for univariate series by V. Gomez and A. Maravall

8. Seasonal adjustment and signal extraction in economic time series by V. Gomez and A. Maravall

PART II: Advanced Topics in Univariate Time Series

9. Heteroscedastic models by R.S. Tsay

10. Nonlinear time series models: Testing and applications by R.S. Tsay

11. Bayesian time series analysis by R.S. Tsay

12. Nonparametric time series analysis: Nonparametric regression, locally weighted regression, autoregression, and quantile regression by S. Heiler

13. Neural network models by K. Hornik and F. Leisch

PART III: Multivariate Time Series

14. Vector ARMA models by G.C. Tiao

15. Cointegration in the VAR model by S. Johansen

16. Identification of linear dynamic multi-input/multi-output systems by M. Deistler Readership: Academic statistics teachers and researchers, statistics practitioners

in industry and government The book is based on lectures given at ECAS '97 (European Courses in Advanced Statistics) in Spain in September 1997, the sixteen chapters being shared between eleven contributors. The stated object is to present an overview of the current status of time series research and practice. The three parts of the book, as listed in the contents, are concerned with basic univariate models and methodology, more modern approaches and multivariate techniques. A web site is given for downloading the data used in the book. The presentation, text, equations, diagrams, etc., is immaculate.

The material is thoroughly and carefully presented, with a list of references at the end of each chapter. The coverage of the subject is quite comprehensive, with most of the standard topics described and analyzed in some detail. There are a few omissions, such as long-range dependence and wavelets, but the book can be seen as a very useful addition to any collection both for learning and reference. Imperial College of Science,

Reviewer: Institute Technology and Medicine Place London, U.K. Name M. Crowder

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Title Practical Statistics and Experimental Design for Plant and Crop Science. Author A.G. Clewer and D.H. Scarisbrick. Publisher Chichester, U.K.: Wiley, pp. xiii + 332, £24.95.

Contents:

1. Basic principles of experimentation

2. Basic statistical calculations

3. Basic data summary

4. The normal distribution, the t-distribution and confidence intervals

5. Introduction to hypothesis testing

6. Comparison of two independent sample means

7. Linear regression and correlation

8. Curve fitting

9. The completely randomised design

10. The randomised block design

11. The Latin square design

12. Factorial experiments

13. Comparison of treatment means

14. Checking the assumptions and transformation of data

15. Missing values and incomplete blocks

16. Split plot designs

17. Comparison of regression lines and analysis of covariance

18. Analysis of counts

19. Some non-parametric methods Readership: Practical, particularly agricultural experimenters, plant and crop scientists, undergraduate students This is an introductory text aimed at students who need to understand statistical analysis of designed experiments, particularly in the agricultural, plant and crop research environments. One of the stated objectives is to encourage students to review the underlying principles of many statistical tests before using them in their research. The text begins with a discussion of the basic ideas of random sampling using several practical sets of data. This is followed by chapters on simple statistical calculations used to summarize data with means and standard deviations. The normal distribution is described and used as the sampling distribution of the sample mean to determine confidence intervals, and the t-distribution is introduced to deal with the case where the standard deviation is estimated from normal samples. There is very little theoretical development, and mathematics is kept to a minimum. The material follows a very traditional 'service course', including tests of hypotheses, one and two sample t-tests, paired t-tests, simple linear regression and a short section on fitting particular non-linear models. Chapters 9 to 12 cover the design and analysis (ANOVA) of simple experiments from completely randomized designs to factorial experiments. The models are almost exclusively fixed effect models, although the term 'random effects' is mentioned briefly. Confounding and fractional replication in factorial experimentation receive very limited explanation. Multiple comparison procedures and treatment contrasts are covered in Chapter 13. Throughout the text the emphasis is on investigation of the assumptions underlying the methods, and later chapters deal with handling situations where the assumptions are violated using transformations or non-parametric methods. Many examples analyzed using Minitab, SAS or Genstat are included. The book could be suitable for a practical course to science students wishing to appreciate statistical methods in agricultural and environmental research.

Reviewer: Institute University of Southhampton Place Southhampton, U.K. Name P. Prescott

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Title Generalized, Linear, and Mixed Models. Author C.E. McCulloch and S.R. Searle. Publisher New York: Wiley, 2001, pp. xxi + 325, £64.50.

Contents:

1. Introduction

2. One-way classifications

3. Single predictor regression

4. Linear models (LMs)

5. Generalized linear models (GLMs)

6. Linear mixed models (LMMs)

7. Longitudinal data

8. GLMMs

9. Prediction

10. Computing

11. Nonlinear models Readership: Statistical modellers, applied statisticians, industrial practitioners This text is to be highly recommended as one that provides a modern perspective on fitting models to data. The emphasis is on the use of maximum likelihood (ML) and restricted maximum likelihood (REML) to fit linear, generalized and mixed models. A complete development of the theory is provided in each case, and there are many practical examples used to illustrate the methodology. The book begins with a review of basic linear models and linear mixed models, then moves on to describe generalized linear models, generalized mixed models and some non-linear models. The models get progressively more difficult, but the use of ML and REML provides a unified approach that extends readily to models based on non-normal distributions such as the Poisson or binomial. The main concepts, including fixed and random effects with both normal and binomial data, are introduced in the early chapter on one-way classification. Single predictor regression methods, including logistic regression, are described for linear and non-linear models with balanced and unbalanced data. These concepts are extended in greater generality and depth in later chapters which cover a variety of different models, linear models, generalized linear mixed models, leading eventually to chapters on handling longitudinal data with linear mixed models, on linear prediction and on the computational issues involved in obtaining ML estimates using the EM algorithm, numerical quadrature and Markov chain Monte Carlo methods. The development relies heavily on matrix algebra and many assumed statistical results with most of these provided in two detailed appendices. The first few chapters would form a graduate level course on modern modelling methods, while the later chapters will provide much useful research material.

Reviewer: Institute University of Southhampton Place Southhampton, U.K. Name P. Prescott

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Title Introducing Anova and Ancova – A GLM Approach. Author A. Rutherford. Publisher B. London: Sage, 2001, pp. ix + 182, £60.00 Cloth; £19.99 Paper.

Contents:

1. An introduction to general linear models: Regression, analysis of variance and analysis of covariance

2. Traditional and GLM approaches to independent measures single factor ANOVA designs

3. GLM approaches to independent measures factorial ANOVA designs

4. GLM approaches to repeated measures designs

5. GLM approaches to factorial measures designs

6. The GLM approach to ANCOVA

7. Assumptions underlying ANOVA, traditional ANCOVA and GLMs

8. Some alternatives to traditional ANCOVA

9. Further issues in ANOVA and ANCOVA Readership: Undergraduate and postgraduate students reading behavioural science, education, psychology or social science Regression techniques and the analysis of (co-)variance (ANOVA/ANCOVA) are probably the most frequently applied of all statistical techniques. Historically, regression and ANOVA developed in different research areas and addressed different questions. Consequently, separate analysis traditions evolved and encouraged the mistaken belief that regression and ANOVA constituted fundamentally different types of statistical analysis. When regression, ANOVA and ANCOVA are expressed in matrix algebra terms, a commonality is evident – this common pattern is the general linear model (the GLM in the title). This text is written to introduce GLMs and, consequently, the book is aimed primarily at the beginning researcher who needs to know how to use the particular techniques presented. It is acknowledged that many readers will not be well-versed in matrix algebra, so scalar algebra and textual descriptions are employed to facilitate comprehension. The text makes little reference to statistical packages, but the author briefly mentions commercially available statistical packages offering GLM programmes. Finally, for the targeted audience, the text will make a valuable addition to any library but, once read, is unlikely to become a reference source.

Reviewer: Institute CEFAS Lowesoft Laboratory Place Lowesoft, U.K. Name C.M. O'Brien

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Title Applying Regression and Correlation: A Guide for Students and Researchers. Author J. Miles and M. Shevlin. Publisher Thousand Oaks, California: Sage, pp. viii + 253, £60.00 Cloth; £19.99 Paper.

Contents:

PART I: I Need To Do Regression Tomorrow

1. Building models with regression and correlation

2. More than one independent variable – multiple regression

3. Categorical independent variables

PART II: I Need To Do Regression Analysis Next Week

4. Assumptions in regression analysis

5. Issues in regression analysis

PART III: I Need To Know More of the Things that Regression Can Do

6. Non-linear and logistic regression

7. Moderator and mediator analysis

8. Introducing some advanced techniques: Multilevel modeling and structural equation modeling

APPENDIX 1: Equations

APPENDIX 2: Doing Regression with SPSS

APPENDIX 3: Statistical Tables Readership: Psychologists This is a beginner's book for psychologists, written by psychologists, "even though it may have the appearance of being about statistics (Preface)." It provides basic commonsense comments and reads well but has little depth regressionwise. The algebra is minimal, the computing is reduced to following SPSS screen photos and there are no exercises. Nevertheless, it serves the purpose of getting psychologists acquainted with regression using a friendly, conversational format.

Reviewer: Institute University of Wisconsin Place Madison, U.S.A. Name N.R. Draper

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Title Applied Logistic Regression, 2nd edition. Author D.W. Hosmer and S. Lemeshow. Publisher New York: Wiley, 2000, pp. xii + 373, £60.95. [Original 1989; Short Book Reviews, Vol. 10, p. 27]

Contents:

1. Introduction to the logistic regression model

2. Multiple logistic regression

3. Interpretation of the fitted logistic regression model

4. Model-building strategies and methods for logistic regression

5. Assessing the fit of the model

6. Application of logistic regression with different sampling models

7. Logistic regression for matched case-control studies

8. Special topics Readership: Graduate students in biostatistics and epidemiology, applied statisticians In the ten years since the first edition of this book [Short Book Reviews, Vol. 10, p. 27], there has been continued research on all statistical aspects of the logistic regression model, together with improvements in the computer software necessary to carry out analyses. Amongst the topics that have been added to this revised edition are: new techniques for model building; an expanded discussion of assessing model performance; and new sections dealing with logistic regression models for multinominal, ordinal and correlated response data, exact methods and sample size issues. Statistical concepts are presented heuristically whenever possible, and mathe–matical details are kept to a minimum. The extensive sets of data discussed and analyzed in the text are available over the Internet via the World Wide Web rather than incorporated in the text as were the data listings included in appendices to the first edition. As in the first edition, the revised text continues to provide a focused introduction to the logistic regression model and its use in methods for modelling the relationship between a dependent categorical variable and a set of covariates.

Reviewer: Institute CEFAS Lowestoft Laboratory Place Lowestoft, U.K. Name C.M. O'Brien

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Title Multidimensional Scaling, 2nd edition. Author T.F. Cox and M.A.A. Cox. Publisher Boca Raton, Florida: Chapman and Hall/CRC, 2001, pp. xi + 308 + disk, £49.99 [Original 1994; Short Book Reviews, Vol. 15, p. 4]

Contents:

1. Introduction

2. Metric multidimensional scaling

3. Nonmetric multidimensional scaling

4. Further aspects of multidimensional scaling

5. Procrustes analysis

6. Monkeys, whisky and other applications

7. Biplots

8. Unfolding

9. Correspondence analysis

10. Individual differences models

11. ALSCAL, SMACOF and Gifi

12. Further m-mode, n-way models

APPENDIX: Computer Programs for Multidimensional Scaling Readership: People who wish to apply multidimensional scaling methods in practice, or who need an introduction to the subject The first edition of this book appeared in 1994. Additional material includes a new chapter on biplots, a discussion of the Gifi approach to nonlinear multivariate analysis and further computer programs. The volume comes with a CD-ROM containing DOS programs, and there is also a discussion of other multidimensional scaling (MDS) software.

MDS methods were originally developed by psychometric researchers, but are now widely used in areas which have included management, marketing, ecology, biology and human computer interaction. Their primary use is as a way of displaying data in a manner which can be conveniently assimilated by the human eye, though they also lead to more formal procedures for defining measurement scales.

The book will provide a good overview of the subject for people who hope to use MDS methods, and will also serve as an introduction to those who wish to explore the methods in more depth.

Reviewer: Institute Imperial College of Science, Technology and Medicine Place London, U.K. Name D.J. Hand

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Title Statistical Decision Theory. Author S. French and D. Rios Insua. . Publisher London: Arnold, 2000, pp. xiii + 301, £40.00.

Contents:

1. Decision theory: An overview

2. Axiomatic bases of decision theory

3. Problem structuring, parameters and attributes

4. Group decisions and export judgement

5. Classical statistical decision theory

6. Bayesian statistical decision theory

7. Decision theory computations

8. Sensitive analysis

9. Sequential statistical decision theory

10. Conclusions Readership: Statisticians, decision theorists This ninth volume in Kendall's Library of Statistics surveys a half-century or more of contributions to both Bayesian and Wald decision theory. The result, in the authors' own words, is an "overview of the main ideas and concepts of statistical decision theory."

The book is modern in outlook and addresses such things as "belief nets", "group decision theory" and Monte Carlo methods of integration. At the same time, classical topics are studied, and a whole chapter is devoted to axiomatic foundations. Inevitably, individual topics must be given, at most, brief coverage. Thus, for example, "invariance" is covered in just three pages. Therefore, readers interested in such topics will need to go to one of the more than four hundred references included in the bibliography for a deeper understanding. In general, this will be a welcome and useful reference book. However, it does not have exercises and would not be suitable for use as a textbook.

Reviewer: Institute University of British Columbia Place Vancouver, Canada Name J.V. Zidek

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Title Structural Equation Modeling: Foundations and Extensions. Author C. Kaplan. Publisher D. Thousand Oaks, California: Sage, 2000, pp. xviii + 215, £33.00.

Contents:

1. Structural equation modeling: An introduction to its history and current practice

2. Path analysis: Modeling systems of structural equations among observed variables

3. Factor analysis

4. Structural equation models in single and multiple groups

5. Statistical assumptions underlying structural equation modeling

6. Evaluating and modifying equation models

7. Multilevel structural equation modeling

8. Latent growth curve modeling

9. Epilogue: Toward a new approach to structural equation modeling and directions for future research Readership: Graduate students in social and behavioural sciences, applied statisticians Structural equation modelling (SEM) represents the hybrid of two separate statistical traditions – factor analysis developed within psychometrics, and the simultaneous equation modelling developed within econometrics. Most of the books discussing SEM concentrate on the practical aspects of the technique and are often nothing more than extended manuals of specific SEM software packages. This text, however, provides a general overview of the theoretical aspects of SEM, a solid discussion of likelihood-based inference for SEM and explains many of the most recent developments in structural equation modelling applied to complex sampling (multi-level SEM and latent variable growth). The reader is assumed to have a good background in statistics that includes multivariate analysis (factor analysis and path analysis) and matrix algebra. Familiarity with, or access to, one of the currently available software packages in SEM would be desirable in order to gain most from the text. SEM is not without its critics principally because it can easily be, and has frequently been, misused. The advanced treatment of SEM presented in this book should, I hope, help to reduce future misuse of the approach.

Reviewer: Institute CEFAS Lowestoft Laboratory Place Lowestoft, U.K. Name C.M. O'Brien

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Title Algebraic Statistics: Computational Commutative Algebra in Statistics. Author G. Pistone, E. Riccomagno and H. Wynn. Publisher Boca Raton, Florida: Chapman and Hall/CRC, 2001, pp. xvii + 160. US$69.95/£46.99.

Contents:

1. Introduction

2. Algebraic models

3. Gröbner bases in experimental design

4. Two level factors: logic, reliability, design

5. Probability

6. Statistical modeling Readership: Scientists with basic background in statistics and Gröbner bases This very challenging monograph demonstrates how Gröbner bases may be used to represent experimental designs, probability models and statistical models. The approach is illustrated with examples involving random variables with few points of support. Casting these problems in an algebraic framework exposes the nature of derived quantities such as conditional expectation. The book points clearly to the future potential use of algebraic tools.

Reviewer: Institute University of Toronto Place Toronto, Canada Name D.F. Andrews

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Title Wavelet methods for time series analysis. Author D.B. Percival and A.T. Walden. Publisher Cambridge University Press, 2000, pp. 594, £40.00/US$69.95.

Contents:

1. Introduction to wavelets

2. Review of Fourier theory and filters

3. Orthonormal transforms of time series

4. The discrete wavelet transform

5. The maximal overlap discrete wavelet transform

6. The discrete wavelet packet transform

7. Random variable and stochastic processes

8. The wavelet variance

9. Analysis and synthesis of long memory processes

10. Wavelet based signal estimation

11. Wavelet analysis of finite energy signals Readership: Electrical engineers, physicists, astronomers, statisticians The authors give a detailed survey of various wavelet methods in Chapters 2 to 6. The remaining chapters are devoted to the applications. The estimation of spectral density function, analysis of long memory processes and the estimation of signals in the presence of correlated noise have also been considered. Students should benefit from the exercises provided at the end of each chapter. This book, together with a recent book by B. Vidakovic (1999), Statistical modelling by Wavelets [Short Book Reviews, Vol. 20, p. 11], provides the readers with a comprehensive methodology. In my opinion the book by Percival and Walden should be available in every university library, and every time-series analyst must read this book for an alternative (to Fourier) set of techniques. University of Manchester Institute of

Reviewer: Institute University of Manchester Science and Technology Place Manchester, U.K. Name T. Subba Rao

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Title Probability and Statistical Models with Applications. Author C.A. Charalambides, M.V. Koutras and N. Balakrishnan (Eds.). Publisher Boca Raton, Florida: Chapman and Hall/CRC, 2000, pp. xxxviii + 624, US$89.95/£59.99.

Contents:

PART I: Approximation, Bounds and Inequalities

1. Nonuniform bounds in probability approximations using Stein's method by L.H.Y. Chen

2. Probability inequalities for multivariate distributions with applications to statistics by J. Glaz

3. Applications of compound Poisson approximation by A.D. Barbour, O. Chryssaphinou and E. Vaggelatou

4. Compound Poisson approximations for sums of dependent random variables by M.V. Boutsikas and M.V. Koutras

5. Unified variance bounds and a Stein-type identity

by N. Papadatos and V. Papathanasiou

6. Probability inequalities for U-statistics by T.C. Christofides

PART II: Probability and Stochastic Processes

7. Theory and applications of decoupling by V. de la Pena and T.L. Lai

8. A note on the probability of rapid extinction of alleles in a Wright-Fisher process by F. Papangelou

9. Stochastic integral functionals in an asymptotic split state space by V.S. Korolyuk and N. Limnios

10. Busy periods for some queues with deterministic interarrival or service times by C. Lefevre and P. Picard

11. The evolution of population structure of the perturbed non-homogeneous semi-Markov systems by P. C.G. Vassilious and H. Tsakiridou

PART III: Distributions, Characterizations, and Applications

12. Characterizations of some exponential families based on survival distributions and moments by M. Albassam, C.R. Rao and D.N. Shanbhag

13. Bivariate distributions compatible or nearly compatible with given conditional information by B.C. Arnold, E. Castillo and J.M. Sarabia

14. A characterization of a distribution arising from absorption sampling by A.W. Kemp

15. Refinements of inequalities for symmetric functions by I. Olkin

16. General occupancy distributions by C.A. Charalambides

17. A skew t distribution by M.C. Jones

18. On the posterior moments for truncation parameter distributions and identifiability by posterior mean for exponential distribution with location parameters by Y. Ma and N. Balakrishnan

19. Distributions of random volumes without using integral geometry techniques by A.M. Mathai

PART IV: Time Series, Linear, and Non-Linear Models

20. Cointegration of economic time series by T.W. Anderson

21. On some power properties of goodness-of-fit tests in time series analysis by E. Paparoditis

22. Linear constraints on a linear model by S.D. Gupta

23. M-methods in generalized nonlinear models by A.I. Sanhueza and P.K. Sen

PART V: Inference and Applications

24. Extensions of a variation of the isoperimetric problem by H. Chernoff

25. On finding a single positive unit in group testing by M. Sobel

26. Testing hypotheses on variances in the presence of correlations by A.M. Mathai and P.G. Moschopoulos

27. Estimating the smallest scale parameter: Universal domination results by S. Kourouklis

28. On sensitivity of exponential rate of convergence for the maximum likelihood estimator by J.C. Fu

29. A closer look at weighted likelihood in the context of mixtures by M. Markatou

30. On nonparametric function estimation with infinite-order flat-top kernels by D.N. Politis

31. Multipolishing large two-way tables by K. Basford, S. Morgenthaler and J.W. Tukey

32. On distances and measures of information: A case of diversity by T. Papaioannou

33. Representation formulae for probabilities of correct classification by W.-D. Richter

34. Estimation of cycling effect on reliability by V. Bagdonavicius and M. Nikulin

PART VI: Applications to Biology and Medicine

35. A new test for treatment vs. control in an ordered 2x3 contingency table by A. Cohen and H.B. Sackrowitz

36. An experimental study of the occurrence times of rare species by M.F. Neuts

37. A distribution functional arising in epidemic control by N.G. Becker and S. Utev

38. Birth and death urn for ternary outcomes: Stochastic processes applied to urn models by A. Ivanova and N. Flournoy Readership: Probabilists, statisticians, scientists working with statistical modeling There are fifty-eight contributors to this volume. The thirty-eight papers were associated with the conference in honour of Professor Theophiles Cacoullos, which was held at the University of Athens, Greece in June 1999

In the preface, the editors write: "This volume has been put together in order to (i) review some of the recent developments in statistical science, (ii) highlight some of the new noteworthy results and illustrate their applications, and (iii) point out possible new directions to pursue."

Part I of this volume contains six articles on approximations, bounds and inequalities; Part II contains five papers on probability and stochastic processes; Part III discusses eight papers on distributions, characterizations and applications; Part IV discusses four papers on time series, linear and non-linear models; Part V includes eleven papers on inference and applications and Part VI is devoted to applications to biology and medicine with four papers.

Beyond minor typographical errors, as with any collection, the chapters differ in depth and complexity. However, the book contains some chapters of interest for probabilists and theoretical and applied statisticians. Most of the articles end with a complete list of references of recent developments, which will make it easier for graduate students and researchers in finding articles of interest.

Reviewer: Institute Isfahan University of Technology Place Isfahan, Iran Name A. Parsian

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Title PROBABILITY VIA EXPECTATION, 4th edition. Author P. Whittle. Publisher New York: Springer-Verlag, 2000, pp. xxi + 353, US$69.95/DM129.00/£46.84.

Contents:

1. Uncertainty, intuition, and expectation

2. Expectation

3. Probability

4. Some basic models

5. Conditioning

6. Applications of the independence concept

7. The two basic limit theorems

8. Continuous random variables and their transformations

9. Markov processes in discrete time

10. Markov processes in continuous time

11. Action optimization: Dynamic programming

12. Optimal resource allocation

13. Finance: 'Risk-Free' trading and option pricing

14. Second-order theory

15. Consistency and extension: The finite-dimensional case

16. Stochastic convergence

17. Martingales

18. Large-deviation theory

19. Extension: Examples of the infinite-dimensional case

20. Quantum mechanics Readership: Students with a basic mathematical faculty, interested in probability The fourth edition still honours the statement made in the Preface to the 1982 Russian Edition: "When this text was published in 1970 I was aware of its unorthodoxy, and uncertain of its reception. Nevertheless, I was resolved to let it speak for itself, and not to advocate further the case there presented." The four editions have indeed spoken out loudly: a clear success in its unorthodoxy, Probability via Expectation has become a treasured classic. My 1976 edition has 239 pages; the over 100 extra pages in this edition are mainly due to various applications of probability theory, including chapters on dynamic programming, optimal resource allocation, option pricing and large-deviation theory. The different axiomatic approach (concentrating first on expectation and only later on probabilities) may for many still seem difficult to swallow in a world where Kolmogorov's triplet is so omnipresent. The dedicated reader should not shy away from this: even thirty years after its first appearance, the approach advocated still has its freshness and intellectual appeal. The applications discussed are interesting to whatever approach one adheres.

Reviewer: Institute ETH-Zürich Place Zürich, Switzerland Name P.A.L. Embrechts

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Title PROBABILITY FOR STATISTICIANS. Author G.R. Shorack. Publisher New York: Springer-Verlag, 2000, pp. xviii + 585, US$79.95/DM159.00/£52.95

Contents:

1. Measures

2. Measurable functions and convergence

3. Integration

4. Derivatives via signed measures

5. Measures and processes on products

6. General topology and Hilbert space

7. Distribution and quantile functions

8. Independence and conditional distributions

9. Special distributions

10. WLLN, SLLN, and series

11. Convergence in distribution

12. Brownian motion and empirical processes

13. Characteristic functions

14. CLTs via characteristic functions

15. Infinitely divisible and stable distributions

16. Asymptotics via empirical processes

17. Asymptotics via Stein's approach

18. Martingales

19. Convergence in law on metric spaces

APPENDIX A: Distribution Summaries Readership: Faculty, researchers and postgraduate students interested in mathematical statistics This book offers a rigorous introduction to measure-theoretic probability with particular attention to topics of interest to mathematical statisticians. There is an unusual coverage with more attention to those probabilistic results used in mathematical statistics and asymptotics, including properties of the quantile and the empirical process, L- and R-statistics, U-statistics, the bootstrap and Skorokhod embedding. The style is mathematical while liberally interspersed with parenthetical remarks (e.g. "Nice!", "Everything else is even more trivial") and acronyms, even in chapter headings. This goes well beyond the traditional results in a first course in probability including Stein's approach on the Central Limit Theorem and is recommended for anyone interested in the probability underlying modern statistics.

Reviewer: Institute Univerity of Waterloo Place Waterloo, Canada Name D.L. McLeish

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Title STATISTICAL METHODS FOR QUALITY IMPROVEMENT, 2nd edition. Author T.P. Ryan. Publisher New York: Wiley, 2000, pp. xxiv + 555, £58.50. [Original 1988]

Contents:

PART I: Fundamental Quality Improvement and Statistical Concepts

1. Introduction

2. Basic tools for improving quality

3. Basic concepts in statistics and probability

PART II: Control Charts and Process Capability

4. Control charts for measurements with subgrouping (for one variable)

5. Control charts for measurements without subgrouping (for one variable)

6. Control charts for attributes

7. Process capability

8. Alternatives to Shewhart charts

9. Multivariate control charts for measurement data

10. Miscellaneous control chart topics

PART III: Beyond Control Charts: Graphical and Statistical Methods

11. Other graphical methods

12. Linear regression

13. Design of experiments

14. Contributions of Genichi Taguchi and alternative approaches

15. Evolutionary operation

16. Analysis of means

17. Using combinations of quality improvement tools Readership: Students in applied statistics and quality engineering; practicing statisticians and engineers in industry This is a significant update of Professor Ryan's textbook from 1988. The chapters on statistical process control and process capability have been expanded considerably to include recent research in the field. The chapter on design of experiments is also longer, with the inclusion of robust design issues. The book is very well written.

While acknowledging the fashions of the day (Six Sigma in this edition, and "Japan's Approach" in the first edition), the author keeps his focus on the statistical issues that are at the heart of quality improvement. Most of the book is dedicated to control charting, process capability, and design of experiments – including Evolutionary Operation.

This book would be suitable for a second course for statistics students who are interested in a career in industry. Many references are provided, giving an up-to-date starting point for getting to know the literature.

Reviewer: Institute --- Place Brookfield, U.S.A. Name C.A. Fung

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Title Optimal Reliability Design. Author W. Kuo, V.R. Prasad, F.A. Tillman and C.-L. Hwang. Publisher Cambridge University Press, 2001, pp. xxi + 389, US$59.95/£37.50.

Contents:

1. Introduction to reliability systems

2. Analysis and classification of reliability optimization models

3. Redundancy allocation by heuristic methods

4. Redundancy allocation by dynamic programming

5. Redundancy allocation by discrete optimization methods

6. Reliability optimization by non-linear programming

7. Metaheuristic algorithms for optimization in reliability systems

8. Reliability-redundancy allocation

9. Component assignment in reliability systems

10. Reliability systems with multiple objectives

11. Other methods for system-reliability optimization

12. Burn-in optimization under limited capacity

13. Case study on design for software reliability optimization

14. Case study on an optimal scheduled-maintenance policy

15. Case studies on reliability optimization

APPENDIX 1: Outline of Dynamic Programming

APPENDIX 2: The Hooke-Jeeves (H-J) Algorithm

APPENDIX 3: Derivation of Polytope U(k+1) from U(k)

APPENDIX 4: Consecutive k-out-of-n Systems Readership: Academic (reliability engineering courses, statistics and operational research); industrial (reliability engineers

in manufacturing industries, e.g. electronics, automotive) The book is a very wide-ranging and thorough treatise on the solution of difficult problems in reliability optimization. A huge field of work is covered on many and varied problems for which it is often extremely difficult to find practical solutions. The writing is clear and careful and reflects the pooled expertise of the four authors. I would recommend this book for both learning and reference.

Reviewer: Institute Imperial College of Science, Technology and Medicine Place London, U.K. Name M. Crowder

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Title Statistical Methods for the Reliability of Repairable Systems. Author S.E. Rigdon and A.P. Basu. Publisher New York: Wiley, 2000, pp. xii + 281, £54.95.

Contents:

1. Terminology and notation for repairable systems

2. Probabilistic models: The Poisson process

3. Probabilistic models: Renewal and other processes

4. Analyzing data from a single repairable system

5. Analyzing data from multiple systems Readership: Engineers and statisticians The first third of the book presents stochastic point processes in a theorem-proof style. Chapter 4 uses graphical methods to examine the intensity function for single repairable systems. It looks at estimation and inference for various intensity processes, together with methods and advice for examining goodness of fit. There is some coverage on standards. Multiple systems are dealt with in Chapter 5, testing for common parameters across the different systems or characterising them by some prior distribution.

Intended for engineers, quality managers and statisticians, this book could also be used for a graduate level course in reliability. There are exercises at the end of each chapter. The book gives a set of methods to be tried; I would have liked more guidance on the most useful approaches to data. Reference to other texts on reliability is patchy: Bain and Engelhart (1991; Short Book Reviews, Vol. 11, p. 43) is included but the classic Lawless (1982; Short Book Reviews, Vol. 2, p. 14) text is not, nor are the more practical texts of Crowder, Kimber, Smith and Sweeting (1991; Short Book Reviews, Vol. 12, p. 6) and Ansell and Phillips (1994; Short Book Reviews, Vol. 15, p. 26), both of which include sections on repairable systems. No link is made to the adaptation of Cox's (1972) survival models to repairable systems, as shown, for example, by Lawless (J.Amer.Statist.Soc., 1987).

Reviewer: Institute CSIRO Place Melbourne, Australia Name R.G. Jarrett

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Title Statistical Analysis of Microstructures in Material Science. Author J. Ohser and F. Mücklich. Publisher Chichester, U.K.: Wiley, 2000, pp. xxii + 381, £60.00.

Contents:

1. Introduction

2. Methodological tools

3. Statistical estimation of basic characteristics

4. Basic characteristics and digitization

5. Covariance and spectral density

6. Size distribution of spherical parts

7. Nonspherical particles of constant shape

8. Size-shape distribution of particles

9. Arrangement of objects

10. Single phase polyhedral microstructures

APPENDIX A: Characteristics of Geometric Objects

APPENDIX B: Software Utilities Readership: Scientists working in materials science, statisticians interested in applications of spatial statistics For statisticians, the book provides many examples of the application of stereology, point process theory and the theory of random sets to questions of practical interest, and suggests how these problems have driven past research and still pose unanswered questions. Comprehensive discussions of Wicksell's problem and of the relationship between x-ray and random set moments appear seldom in the statistics literature, but these topics are comprehensively presented here. For materials scientists, the book gives an introduction to the analysis of two-dimensional and three-dimensional microscopic images, organized by the form of the data and utilizing up-to-date statistical techniques. Subroutines (in C) for calculating statistics that are not generally found in statistics packages are given, and much detail is given to the practicalities of estimating from digitized data statistics that describe continuous surfaces. The references are comprehensive, pointing to the derivations and proofs that this book has no room to contain.

Reviewer: Institute University of Maryland Place College Park, U.S.A. Name J.D. Picka

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Title Non-linear and Nonstationary Signal Processing. Author W.J. Fitzgerald, R.L Smith, A.T. Walden and P.C. Young (Eds.). Publisher Cambridge University Press, 2001, pp. ix + 471, £60.00/US$95.00.

Contents:

1. Bayesian computational approaches to model selection by C. Andieu, A. Doucet, W.J. Fitzgerald and J.M. Perez

2. Sequential analysis of non-linear dynamic systems using particles and mixtures by N. Gordon, A. Marrs and D. Salmond

3. Stochastic, dynamic modelling and signal processing: Time variable and state dependent parameter estimation by P. Young

4. The use of generalised likelihood measures for uncertainty estimation in higher order models of environmental systems by K. Beven, J. Freer, B. Hankin and K. Schulz

5. Spatial statistics in environmental science by R.L. Smith

6. Useful lies: Dynamics from data by A. Mees

7. A modelling framework for the prices and times of trades made on the New York stock exchange by T.H. Rydberg and N. Shepherd

8. The sample autocorrelations of financial times series models by R.A. Davis and T. Mikosch

9. The many roads to time frequency by P. Flandrin

10. Multiple window time varying spectrum estimation by M. Bayram and R. Baraniuk

11. Multitaper analysis of nonstationary and non-linear time series data by D.J. Thomson

12. Signal and image denoising via wavelet threshholding: Orthogonal and biorthogonal, scalar and multiple wavelet transform by V. Strela and A. Walden

13. Wavestrapping timeseries: Adaptive wavelet-based bootstrapping by D.B. Percival, S. Sardy and A.C. Davison Readership: Statisticians, applied mathematicians, communication engineers This volume contains thirteen papers (including four from the editors) by authors who participated and presented at one or more workshops held at the Newton Institute, Cambridge, during July-December 1998, as a part of the excellent programme on non-linear signal processing organized by the four editors. There is a heavy emphasis on Bayesian methodology (especially MCMC techniques) in signal extraction, wavelet methods in denoising and decorrelation of signals (also applied to long memory processes). Of course readers should remember that decorrelation (by whatever means) does not achieve much if the signals are non-linear and hence nonGausian, and this was never pointed out either here or anywhere else in the literature. Nowhere in the volume are the terms nonlinearity and nonstationarity defined. This is rather surprising when this volume is meant to address these topics. The papers included clearly reflect the interest of the editors and their view of analyzing such signals, but others may have alternative ideas. I found some papers difficult to understand even though they are supposed to be review papers. Despite these small reservations, I have no doubt many readers will find this volume useful.

Reviewer: Institute University of Manchester Institute of Science and Technology Place Manchester, U.K. Name T. Subba Rao

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Title Difference Equations with applications to Queues. Author D.L. Jagerman. Publisher New York: Dekker, 2000, pp. 246, £89.74.

Contents:

1. Operators and functions

2. Generalities on difference equations

3. Nörlund sum: Part one

4. Nörlund sum: Part two

5. The first-order difference equation

6. The linear equation with constant coefficients

7. Linear difference equations with polynomial coefficients Readership: Mathematicians, researchers and postgraduates interested in difference equation methods and in queues This monograph presents the author's Nörlund sum generalization of the Nörlund integral; this gives a solution of the Äù F(ù)=ø(ù) which reduces, as ù>0, to a solution of the differential equation DF(ù)=ø(ù). The author also develops a U-operator method analogous to the Lie-Gröbner method for differential equations; it provides approximate solutions for functional equations of the form G(ø(z))–l(z)G(z)=m(z). The Milne-Thomson operators, ð and ñ, are applied in the final chapter to linear difference equations with polynomial coefficients, giving solutions in terms of factorial series.

The power of the author's methods is demonstrated via certain queuing models. There is no attempt, however, to give a comprehensive coverage of work on difference equations arising from queues; for example, for the exact solution of the transient M/M/1 queue the reader is referred only to Saaty.

The exercises are for self-study and rarely relate to queues. I was disappointed that the sole mention of a q-difference equation is in one of them.

Reviewer: Institute University of St. Andrews Place St. Andrews, U.K. Name A.W. Kemp

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Title Complex Stochastic Systems. Author O.E. Barndorff-Nielsen, D.R. Cox and C. Klüppelberg (Eds.). Publisher Boca Raton, Florida: Chapman and Hall/CRC, 2001, pp. xv + 279, US$64.95/£43.99.

Contents:

1. A primer on Markov chain Monte Carlo by P.J. Green

2. Causal inference from graphical models by S.L. Lauritzen

3. State space and hidden Markov models by H.R. Künsch

4. Monte Carlo methods on genetic structures by E.A. Thompson

5. Renormalization of interacting diffusions by F. den Hollander

6. Stein's method for epidemic processes by G. Reinert Readership: Researchers and research students seeking an introduction to modern statistical work in complex stochastic systems This book contains revised versions of the main papers presented at the 4th Séminaire Européen de Statistique on 'Complex Stochastic Systems'. It is intended to be tutorial in style. I shall not attempt to review all of the chapters, but simply describe the first three. Chapter 1, on Markov chain Monte Carlo is 'a primer for (those) seeking to get started in some aspect of MCMC research'. The author makes clear that it is not aimed at those who wish to use standard software, but rather for those who wish to develop their own. Chapter 2 describes issues of causality, which have recently attracted renewed interest within the statistical community. The author points out that graphical models provide a convenient structure within which to discuss such concepts, and describe such models, beginning with the fundamental, but sometimes ignored, distinction between conditioning by intervention, and conditioning by observation. Chapter 3 describes hidden Markov models, which continue to have a major impact on modern statistics. The author illustrates the range of applications and also describes estimation methods.

One often has reservations about edited volumes, but this one is an excellent introduction to some of the most important tools of modern statistics.

Reviewer: Institute Imperial College of Science, Technology and Medicine Place London, U.K. Name D.J. Hand

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Title Weakly Dependent Stochastic Sequences and Their Applications. Volume XI: Censorship Under Weak Dependence. K. Yoshihara. Author Tokyo: Sanseido, 2000, pp. vii + 377. Publisher Contents:

1. Foundation

2. Order statistics under weak dependence

3. Asymptotic properties of K-M estimators

4. K-M integrals for censored dependent data

5. Nonparametric estimators of d.f.'s

6. M-estimators for hazard functions Readership: Researchers in survival analysis This nicely edited book is Volume XI in a series by the same author. These books deal with classical topics like partial sums, order statistics, density estimation, bootstrap, etc. in the situation of weak dependence. The present volume deals with the analysis of survival data and more specifically with the celebrated Kaplan-Meier estimator for the survival function. Since the original paper in 1958, there have been numerous papers with properties, extensions, modifications, etc. The present book studies properties on the Kaplan-Meier estimator in the case where the underlying survival times are dependent (and subject to random right censorship). Also, recent papers on Kaplan-Meier integrals, density functions, hazard rates, etc. are reconsidered from the viewpoint of weak dependence. The book contains no real data examples but is rather an interesting collection of theoretical results.

Reviewer: Institute Limburgs Universitair Centrum Place Diepenbeek, Belgium Name N.D.C. Veraverbeke

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Title Option Pricing and Portfolio Optimization, Modern Methods of Financial Mathematics. Author R. Korn and E. Korn. Publisher Providence, Rhode Island: American Mathematical Society, 2000, pp. xiii + 251, US$39.00.

Contents:

1. Frequently used notation

2. The mean-variance approach in a one-period model

3. The continuous-time market model

4. Option pricing

5. Pricing of exotic options and numerical algorithms

6. Optimal portfolios Readership: Advanced undergraduates in mathematics and statistics, graduates in financial economics The aim of the book is to introduce Itô calculus to solve problems in modern finance. Therefore, the authors forsake generality which is not needed in the applications. Instead, the authors' scope is to write a self-contained book where mathematical results are not simply cited, but the concepts are developed and complete proofs are given. This approach, together with the well chosen finance topics and examples, makes the book especially useful for students seeking a lively introduction to Itô calculus.

Reviewer: Institute Zürcher Kantonalbank Place Zürich, Switzerland Name P. Vanini

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Title Operations Research: A Practical Introduction. Author M.W. Carter and C.C. Price. Publisher Boca Raton, Florida: CRC Press, 2001, pp. viii + 394, US$29.99.

Contents:

1. Introduction to operations research

2. Linear programming

3. Network analysis

4. Integer programming

5. Nonlinear optimisation

6. Markov processes

7. Queuing models

8. Simulation

9. Decision analysis

10. Heuristic techniques in operations research Readership: Operational researchers To quote the authors, 'This book is designed as an introductory text course in Operations Research.' The chapter on heuristics techniques is particularly welcome. The mathematical skills required are an understanding of calculus and matrix algebra notation. The practical components of this text that are associated with each chapter are a Guide to Software Tools, and some illustrative case studies. The software sections give brief descriptions of the most popular commercial products; however, no contact information, for example web site or e-mail address, is given. This reviewer finds it strange that the authors describe both the intricacies of the techniques and available software yet barely mention the user interface, for instance modelling languages in mathematical programming. The illustrative case studies are one page summaries of published applications; to fully understand the power of the techniques it is necessary for the user to read the original paper.

Reviewer: Institute London School of Economics Place London, U.K. Name S. Powell

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Title Sampling Methodologies with Applications. Author P.S.R.S. Rao. Publisher New York: Chapman and Hall/CRC, 2000, pp. xxii + 311. US$69.95/£43.99.

Contents:

1. Introduction

2. Simple random sampling: Estimation of means and totals

3. Simple random sampling: Related topics

4. Proportions, percentages, and counts

5. Stratification

6. Subpopulations

7. Cluster sampling

8. Sampling in two stages

9. Ratio and ratio estimators

10. Regression estimation

11. Nonresponse and remedies

12. Further topics Readership: Students and practitioners with one or two courses in basic theoretical and applied statistical concepts This book covers most of the commonly used methods in sample surveys as well as some recent developments. The level of the book is elementary. Appendices at the end of each chapter present some mathematical derivations of basic results. The notation used is sometimes not consistent (for example, Appendices A2 and A3 on the properties of random variables, and the notation for combinatorics). Numerical examples illustrate the algebraic formulae. Examples of case studies illustrating how the survey methods are used in more complicated situations would be beneficial. The book includes useful references for further study, especially for recent developments in survey sampling methods.

Reviewer: Institute University of Wiscosin Place Madison, U.S.A. Name K.-W. Tsui

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Title STATISTICAL PROCESS CONTROL IN INDUSTRY: IMPLEMENTATION AND ASSURANCE OF SPC. R.J.M.M. Does, K.C.B. Roes and A. Trip. Author Dordrecht, The Netherlands: Kluwer, 1999, pp. xi + 231, DFL195.00/US$117.00/£69.00. Publisher Contents:

Introduction

1. SPC: A historical perspective

2. SPC as part of quality policy

3. Implementation plans for SPC

4. Introducing SPC with teams

5. The plan of action for introducing SPC

6. Principles of the Shewhart type of control charts

7. Designing control charts to support improvement

8. Control charts

9. Tools for solving problems

10. From control to assurance

11. Software for SPC

12. SPC competition and self-evaluation Readership: Managers and engineers seeking guidance on how to initiate statistical process control in manufacturing This book is a revised translation of the original Dutch work from 1996. It presents statistical process control (SPC) as a philosophy for operations management on the shop floor. The statistical methods employed are the standard, simple, well-established ones.

The work is more a prescription for management strategy than a statistical textbook. The authors offer a

plan of activities by which companies with no prior experience with SPC can get started, developed from their experiences with three Dutch companies. Examples are given of SPC in mass production, in low volume production, and in very low volume production. The main value of the book is in the direction it can offer to non-statistical managers who want to help introducing SPC, but the reader will need to take care to adapt the authors' recommendations to their own companies' cultures.

Statistics itself occupies less than half the book. Not much numeracy is expected of the reader, but there are occasional, surprising, excursions into statistical theory in this volume which presumably was aimed at managers. The book would be a useful companion to be read together with a more traditional SPC text.

Reviewer: Institute --- Place Brookfield, U.S.A. Name C.A. Fung

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Title STATISTICAL MODELLING WITH QUANTILE FUNCTIONS. Author W.G. Gilchrist. Publisher Chapman and Hall/CRC, 2000, pp. xx + 320, US$69.95.

Contents:

1. An overview

2. Describing a sample

3. Describing a population

4. Statistical foundations

5. Foundation distributions

6. Distributional model building

7. Future distributions

8. Identification

9. Estimation

10. Applications

11. Regression quantile models

12. Bivariate quantile distributions

13. A postscript

APPENDIX 1: Some Usefull Mathematical Results

APPENDIX 2: Further Studies in the Method of Maximum Likelihood

APPENDIX 3: Bivariate Transformations Readership: Statisticians, scientists working with statistical modelling From the book preface: "This book looks at statistical modelling from a different perspective." The book deals with the steps of the statistical modelling process, using quantile methods, as a tool for problem solving. In the first chapter, the author gives a very good overview of the subject covering an overall process of background construction, identification, estimation, validation and application to show "the wood for the trees". No attempt has been made to apply the recommended approaches to a real large set of data as a sample. The reason mentioned by the author (p. 167) is for the sake of saving space, but it would

have been worth while to have added some extra pages. Beyond minor typographical and some notational errors, the book is a good introduction to the subject and will serve statisticians, researchers, etc. in their modelling work. The researchers will undoubtedly gain a lot of knowledge and insight of the core modelling ideas and techniques by reading this book.

I enjoyed reading this book; it is well written, easy to read and it would be worth considering as a text for honour students or as a seminar course at a graduate level.

Reviewer: Institute Isfahan Univerisity of Technology Place Isfahan, Iran Name A. Parsian

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Title Annotated readings in the history of statistics. Author H.A. David and A.W.F. Edwards. Publisher New York: Springer-Verlag, 2001, pp. xv + 252, US$69.95/DM151.00.

Contents:

1. The introduction of the concept of expectation (Pascal, 1654)

2. The first formal test of significance (Arbuthnott, 1710)

3. Coincidences and the method of inclusion and exclusion (Montmort, 1713; N. Bernoulli, 1713; deMoivre, 1718)

4. The determination of the accuracy of observations (Gauss, 1816)

5. The introduction of asymptotic relative efficiency (LaPlace, 1818)

6. The logistic growth curve (Verhulst, 1845)

7. Goodness-of-fit statistics (Abbe, 1863)

8. The distribution of the sample variance under normality (Helmert, 1876)

9. The random walk and its fractal limiting form (Venn, 1888)

10. Estimating a binomial parameter using the likelihood function (Thiele, 1889)

11. Yule's paradox ("Simpson's paradox") (Yule, 1903)

12. Beginnings of extreme-value theory (Bortkiewicz, 1922; von Mieses, 1923)

13. The evaluation of tournament outcomes (Zermelo, 1929)

14. The origin of confidence limits (Fisher, 1930)

APPENDIX A: English Translations of Papers and Book Extracts of Historical Interest (Bibliography)

APPENDIX B: First (?) Occurrence of Common Terms in Statistics and Probability Readership: Statistics history enthusiasts The preface tells us that "Interest in the history of statistics has grown substantially in recent years..." How can we tell? It is true that the number of historical publications has grown, but how many people actually read them and what do they get out of them? Do you really want to read today a translation of a paper that E. Zermelo wrote in German in 1929 about the playing strengths of chess players in a tournament? (The underexplained example in that article refers to the famous New York 1924 tournament; however, chess players may be puzzled about what we can learn from the relevant "playing strengths" given, since they mirror the tournament order exactly.) If at this point in the review you are becoming annoyed with the reviewer's apparent attitude and are saying impatiently, "Of course we should study this sort of history!", you will enjoy this book very much. H.A. David translated three articles from the original French, six articles from German, and one from Latin; A.W.F. Edwards translated one from French; and S.L. Lauritzen one from Danish. Five more articles are reproduced in their original English. Each article is introduced by an essay called "Comments on…"; these comments are informative, interesting and beautifully written, and contain numerous modern connected references. The production is first class. H.A. David has used parts from this book "in a short course on the history of statistics, recently, given at Iowa State University." The collection is fun to browse. Statistics history buffs and browsers should order this book immediately.

Reviewer: Institute University of Wisconsin Place Madison, U.S.A. Name N.R. Draper

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Title The Lady Tasting Tea. How Statistics Revolutionized Science in the Twentieth Century. Author D. Salsburg. Publisher New York: Freeman, 2001, pp. xi + 340, US$23.95.

Contents:

1. The lady tasting tea (Fisher, Design of Experiments)

2. The skew distributions (Galton and Karl Pearson)

3. That dear Mr. Gosset (Student t, and both K. Pearson and Fisher)

4. Raking over the muck heap (Fisher at Rothamsted)

5. "Studies in crop variarion" (Anova and controlled randomisation)

6. "The hundred year flood" (Tippett and E.J. Gumbel)

7. Fisher Triumphant (The logic of Inductive Inference, 1934)

8. The dose that kills (Bliss and Probits)

9. The bell shaped curve (Lindeberg, Lévy, Höffding)

10. Testing the goodness of fit (Neyman)

11. Hypothesis testing (Neyman and E.S. Pearson)

12. The confidence trick (The AIDS epidemic and confidence sets)

13. The Bayesian heresy (Mosteller and Wallace, de Finetti and Savage)

14. The Mozart of mathematics (Kolmogoroff)

15. The worms eye view (F.N. David)

16. Doing away with parameters (Wilcoxon, Chernoff and Savage, Pitman)

17. When part is better than the whole (biased sampling; Mahalanobis)

18. Does smoking cause cancer? (Doll and Hill vs Fisher)

19. If you want the best person (Gertrude Cox)

20. Just a plain Texas farm boy (S.S. Wilks)

21. A genius in the family (I.J. Good)

22. The Picasso of statistics (J.W. Tukey)

23. Dealing with contamination (G.E.P. Box)

24. The man who remade industry (W. Edwards Deming)

25. Advice from the lady in black (S.V. Cunliffe)

26. The march of the martingales (Lévy, Aalen, Andersen, Gill, Olshen)

27. The intent to treat (Peto, Cox, Box, and Rubin)

28. The computer turns upon itself (Efron)

29. The idol with feet of clay (Kuhn)

Afterword, timeline Readership: Anyone interested in statistics, especially statistics students The parentheses are reviewer's additions, indicating topics discussed.

A very unusual book, containing many excellent accounts of statistics in practice. The preface and some other chapters discuss deep issues of statistical philosophy. A fair number of amusing errors, e.g. neither of the Guinness family's two peers was Lord Guinness. A most interesting read.

Reviewer: Institute University of Essex Place Colchester, U.K. Name G.A. Barnard

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Title The Subjectivity of Scientists and the Bayesian Approach. Author S.J. Press and J.M. Tanur. Publisher New York: Wiley, 2001, pp. x + 274, £57.50.

Contents:

1. Introduction

2. Selecting the scientists

3. Some well-known stories of extreme subjectivity

4. Stories of famous scientists

5. Subjectivity in science in modern times: The Bayesian approach

APPENDIX: References by Field of Application for Bayesian Statistical Science Readership: Professional scientists and the general public with an interest in science, in scientists, and in the methods

that scientists use This book describes the role of subjectivity and preconceptions in science, via a series of vignettes illustrating how famous scientists in history achieved their major advances. Chapter 3 briefly describes how Kepler, Mendel, Millikan, Burt and Mead allowed their preconceptions to influence the data they chose to use on which to base their conclusions (or how they distorted or manufactured data to match their preconceptions). Chapter 4 describes the work of Aristotle, Galileo, Harvey, Newton, Lavoisier, Von Humboldt, Faraday, Darwin, Pasteur, Freud, Curie and Einstein. Each of the sections in Chapter 4 is divided into a brief historical sketch, an outline of their scientific contribution, a list of their major works, and a discussion of the role of subjectivity in the work. Most of these people are now regarded as having made a major contribution, but some of them are now regarded as little better than examples of self-deception. It is interesting to have them all examined from the same perspective, in which their preconceptions drive their theoretical developments.

As far as the role of bias, preconceptions and subjectivity is concerned in science, this book is fascinating. However, in many of the cases it seems contrived to attach it to today's formal methods of Bayesian inference.

Reviewer: Institute Imperial College of Science, Technology and Medicine Place London, U.K. Name D.J. Hand

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Title Mathematics of Chance. Author J. Andel. Publisher Chichester, U.K.: Wiley, 2001, pp. xxiii + 235, £39.50.

Contents:

Introduction

1. Probability

2. Random walk

3. Principle of reflection

4. Records

5. Problems that concern waiting

6. Problems that concern optimisation

7. Problems on calculating probability

8. Problems on calculating expectation

9. Problems on statistical methods

10. The LAD method

11. Probability in mathematics

12. Matrix games Readership: All students of probability theory, applied statisticians in industry This is a compilation of interesting and popular problems concerning mainly probability theory, with some statistics. The material is very accessible, in the most part requiring no more than basic elements of calculus. While there are many old favourites here, there are some novelties and some problems given a new slant through references to, for example, Olympiad problems and those which have appeared in the American Mathematical Monthly. The book is a translation and modification of the original Czech edition. There are some glitches as a result ('dice' as singular…), but most are not crucial. The problems inspire the reader to follow up references and the style is generally very engaging.

This is a very useful supplement to Problems and Snapshots from the World of Probability (Blom, Holst and Sandell – Springer-Verlag [1994; Short Book Reviews, Vol. 14, p. 22]) and the classic Fifty Challenging Problems in Probability (Mosteller – Addison Wesley, 1965, Dover, 1987).

Reviewer: Institute Imperial College of Science, Technology and Medicine Place London, U.K. Name F.H Berkshire

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Title Encyclopedia of Epidemiological Methods. Author M.B. Gail and J. Benichou. Publisher Chichester, U.K.: Wiley, 2000, pp. xxi + 978, £235.00.

Contents:

From Absolute Risk to Vital Statistics Readership: Epidemiologists, statisticians working in epidemiology This volume contains a selection of excellent articles on many of the concepts, methods and tools that researchers working in epidemiology require. It is difficult to judge whether the selected topics would satisfy all appetites, as the field is becoming richer and more diversified. However, when consulting this volume regularly over the last two months, while investigating new projects and supporting students' dissertations, I have always found comprehensive and clear overviews at hand.

All entries are linked to each other by web-style cross-referencing and are enriched by up-to-date, but also historical, references for more in-depth reading. Most of the contributions are also rich of valuable insights in the topic, although sometimes the "encyclopaedic" style becomes rigid and too many classifications and sub-classifications are offered, for instance with differing listings of types of bias.

The articles are written by experts based in North America, Europe, Australia, New Zealand and Japan. Hence there is a wide perspective on several of the topics, as well as some differences. Many of the methodological articles already appeared in the Encyclopedia of Biostatistics but others have been added to cover specific issues, such as "birth cohort studies" and "cancer registries", or to introduce emerging or expanding fields, such as "genetic epidemiology". Some large topics, like case-control studies, have several articles from different authors which are often linked-up by short enlightened entries from one of the editors.

There is no doubt that this Encyclopedia dedicated to methods in epidemiology is an invaluable tool for researchers involved in medical and public health research as it offers an excellent springboard for acquiring and strengthening practical and methodological tools.

Reviewer: Institute London School of Hygiene and Tropical Medicine Place London, U.K. Name B.L. De Stavola

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Title An Introduction to randomized controlled clinical Trials. Author J.N.S. Matthews Publisher London: Arnold, 2000, pp. xiv + 189, £19.99.

Contents:

1. What is a randomized controlled trial?

2. Bias

3. How many patients do I need?

4. Methods of allocation

5. Assessment, blinding and placebos

6. Analysis of results

7. Monitoring accumulating data

8. Subgroups and multiple outcomes

9. Protocols and protocol deviations

10. Some special designs: Crossover, equivalence and clusters

11. Meta-analyses of clinical trials Readership: Undergraduate and postgraduate students of statistics Over recent decades, randomized controlled clinical trials have become established as the method used to assess new treatments if claims of the efficacy of a treatment are to find widespread acceptance. This book provides an introduction to the statistical methodology that underpins the randomized controlled trial. Administrative aspects of running a trial receive little emphasis in the text but there are many excellent books on clinical trial methodology that the interested reader may consult. Trials with binary outcomes are given less prominence in the text than might be expected from their prevalence in medical practice, and survival analysis is completely omitted. Sections in the text that contain more advanced material are clearly identified with an asterisk so that such material might be omitted on a first reading – thus allowing use of the text within courses to be tailored to the needs of the student audience. The text assumes no underlying medical background, is well written and easy to read.

Reviewer: Institute CEFAS Lowesoft Laboratory Place Lowesoft, U.K. Name C.M. O'Brien

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Title An Introduction to Probability and Statistics, 2nd edition. Author V.K. Rohatgi and A.K.M.E. Saleh. Publisher New York: Wiley, 2001, pp. xv + 716, £67.95.

Contents:

1. Probability

2. Random variables and their probability distributions

3. Moments and generating functions

4. Multiple random variables

5. Some special distributions

6. Limit theorems

7. Sample moments and their distributions

8. Parametric point estimation

9. Neyman-Pearson theory of testing of hypothesis

10. Some further results of hypothesis testing

11. Confidence estimation

12. General linear hypothesis

13. Nonparametric statistical inference Readership: Students taking postgraduate or final year undergraduate courses in mathematics The book consists of three parts namely: (a) the core of the probability; (b) foundations of statistical inference; and (c) Chapters 11 to 13 on special topics. There is a wealth of material and, although the topics are of a conventional nature, the discussions and special topics are unique. Most of the presentations give far more depth than one would expect in a text of this type. There are five hundred and fifty problems with three hundred and fifty worked examples and one hundred and fifty references included in this comprehensive textbook. The mathematical prerequisites to reading this book are that the students should have had basic courses in linear algebra, set theory and a good background in calculus. This text is for mathematics specialists and not recommended as a service textbook.

Reviewer: Institute South Bank University Place London, U.K. Name S. Starkings

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Title Statistical Methods in Spatial Epidemiology. Author A.B. Lawson. Publisher Chichester, U.K.: Wiley, 2001, pp. x + 277, £55.00.

Contents:

PART I: The Nature of Spatial Epidemiology

PART II: Important Problems in Spatial Epidemiology Readership: Graduate statisticians The author has been a major influence in the development of statistical methods in epidemiology, and has been the principal editor of one recent major volume of papers on disease mapping and spatial epidemiology (Disease Mapping and Risk Assessment for Public Health [noted, Short Book Reviews, Vol. 19, p. 52]), 