Excellent book for graduate students in statistics. Though a little high in price, it will prove worth every penny and plus a lot more. The author is unusual in the way he presents the material; the clarity of thought is outstanding. There a are a lot of good statistics books out there,but a few are able to reach students in their minds and make everything click! this text is definitely one that will shed light on the parts of statistics that are most dreaded by students.

Robert Haining's book is the definitive source for spatial data analysis. He provides an excellent introduction to spatial statistics which leads naturally to discussion of the more advanced methodologies available to researchers. Well recommended for geographers with some familiarity with inferential statistics

Defines what a SPC (statistical process control) system can do, shows how to diagnose it and conduct an audit, provides corrective actions and a four step methodology, and presents the five universal SPC principles. Includes a glossary. A valuable, accessible how-to book. Reviewed by Gerry Stern, Co-founder, Stern & Associates.

Spectral Analysis and Its Applications is an excellent source for professionals with intermediate time-series statistics skills. It clearly outlines and demonstrates the methods necessary to carry out sound spectral analysis of time-series data and how to use this data for modelling purposes. It is also a great aid and reference for anyone who needs the means to triumph over inexperience and under-education in time-series debates. Four years out of graduate school, I need this book again; I wish I'd bought it sooner.

Koopmans is king when it comes to statistical analysis. Read this book.

The authors present an exceptionally clear treatment of Clifford algebras, Clifford C*-algebras, and von Neumann Clifford algebras, their Fock representations and related developments. The proofs are concise and to the point.

Lloyd provides an excellent and detailed presentation of techniques for the analysis of categorical data. This is an advanced course intended for graduate statistics students in the US and masters level students in the British and Australian systems.

Each chapter in the book is well written and has real examples to illustrate concepts. At the end of each chapter is a section summarizing related references to the literature by sections in the chapter. This is then followed by a large set of exercises that can be used for homework or test problems. Many of the exercises incorporate data from interesting real world problems. Many of the problems can be solved with the software GLIM. Other software including Minitab, SPlus, StatXact and LogXact are used from time to time. Manuals for the software and some S algorithms are provided on a Wiley web site set up for the text and described in the Preface.

Chapter 1 starts out with the basic parametric theory of statistics including maximum likelihood, asymptotic distribution theory, hypothesis testing and goodness of fit measures. It also includes a section on transformations of data and parameters.

Chapter 2 describes categorical data and the general multinomial model. The Poisson model is also discussed and goodness of fit procedures are described. Confidence intervals and hypothesis testing are also included. A connection between the multinomial and Poisson models is given.

Chapter 3 deals with the special case of binary data. Contingency tables and binomial models are discussed. Methods for comparing two or more binomial populations are covered as are the testing of independence in a 2x2 contingency table and the comparison of several 2x2 tables. Generalized linear models are introduced here as a tool for dealing with these problems. Lloyd is careful not to gloss over important issues and he includes discussion of hidden factors. Simpson's paradox is covered in Section 3.6. Asymptotic chi-square distributions are often needed for these unconditional tests.

Chapter 4 covers binomial regression models, issues of overdispersion and the use of the normal general linear model as an approximation. Weighted least squares using the inverse of the variance for weights is presented. Chapter 5 adds nonparametric smoothing to the tools for binary data.

Chapter 6 covers Poisson regression models. There is also some discussion fo the treatment of ordinal data as well as categorical data in Chapter 6. Models for incomplete data are also treated in Chapter 6. Log linear models and multinomial logit models are also considered in Chapter 6.

Chapter 7 deals with the simpler conditional tests. These tests are sometimes preferred because of their simplicity and because they have properties of exactness due to the conditioning on the observed data that reduces the class of possible probability models. These procedures have the advantage of being distribution free as well as exact but also have the slight disadvantage of being computer-intensive. Lloyd is careful to point out the advantages of tests based on conditional inference and the available software that makes these techniques very practical. However, he should also be commended for pointing out that the conditioning argument is not universally accepted. A point that is often neglected when dealing with permutation tests like Fisher's exact test. The chapter also includes an interesting section on saddlepoint approximations that can be used to get a good analytic approximation to the exact procedure.

Good features of the book include, the careful and clearly stated theoretical ideas and the wide range of applications used to show practicality of the methods. Important questions related to outliers and missing data are properly covered.

Statistical methodology in medicine is growing and becoming more sophisticated. Biostatistician have been challenged by recent problems and data sources to develop new methods. Many leading statisticians have contributed interesting and important papers in this volume. The list of authors includes Aalen, Borgan, Diggle, Dunn, Everitt, Fitzmaurice, Hedges, Langholz and Spiegelhalter among others.

The thirteen papers include an overview with historical discussion of four influential statisticians and current "hot" topics by Everitt and Dunn, coverage of fraility models by Aalen, tree structured survival analysis by Segal, longitudinal studies with non-normal responses by Davis, missing data in longitudinal studies by Diggle, and Bayesian meta-analysis by Hedges. All the papers include case studies or examples and provide many helpful references. Topics can be categorized under the subjects (1) Survival Analysis, (2) Longitudinal Data Analysis, (3) Bayesian Methods and (4) Statistics in Image Processing.

This is a great reference for research statisticians and practicing biostatisticians.

This is a well-written, beautifully presented, useful and interesting book. It explains how materials scientists can analyse and characterize microstructures in 3D materials, using modern statistical methods from 'stochastic geometry'. The authors are respectively a mathematical statistician and a materials scientist. The exposition is excellent, and goes all the way from basic theory to practical applications, including free software on their web site. The presentation is a good balance of theory, verbal explanation and practical examples. The illustrations and diagrams are very effective, and include some superlative 3-D pictures. The software is nicely presented and explained, also in relation to the equations. The topics covered include basic stereology, texture characterisation, digital image measurement, covariance and spectral techniques, size and shape distributions of particles, spatial arrangement of objects, and polyhedral single-phase microstructures. I found only a few tiny typos so far. Well worth the hardback price - and the hardback binding will take a beating before I'm done with it!

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