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This book covers the theoretical developments and applications of sequential hypothesis testing and sequential quickest changepoint detection in a wide range of engineering and environmental domains. It reviews recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic

A collection of vignettes that examines statistics. It covers major areas of application and discusses theory and methods.

A revised textbook covering all aspects of risk theory in a practical way. It follows on from the late R.E. Beard's book "Risk Theory" and should be of interest to actuarial students and practitioners working in the insurance industry as well as economists and applied statisticians.

Helps researchers understand the theory of the design of experiments so they can easily adapt general principles to their specialties. This book brings the theory to non-statisticians at a reasonable mathematical level so that they can apply and adapt the special designs.

The components of variance is a notion useful to statisticians and quantitative research scientists working in a variety of fields, including the biological, genetic, health, industrial, and psychological sciences. This book focuses on developing the models that form the basis for analyses as well as on the statistical techniques themselves.

Offers a description of singular spectrum analysis (SSA) general theory and methodology. This book introduces the basic concepts and presents a detailed treatment of the methodology. It covers the theory of SSA. It offers an opportunity to obtain a working knowledge of why, when, and how SSA works.

An exploration of the many different bootstrap techniques. It discusses useful statistical techniques through real data examples and covers nonparametric regression, density estimation, classification trees, and least median squares regression. There are numerous exercises.

Quantal response (or dose-response) data arise in many areas, both through experimental and observational studies. Particular emphasis is placed in this book on applications in bioassay and toxicology. Throughout, the material is motivated by a range of practical examples.

This monograph brings together the older and newer ideas on the analysis of survival data to present a comprehensive account of the field.

This is a collection of papers given at the 2nd Seminaire European Statistique. It aims to give talented young researchers the opportunity to get quickly to the forefront of knowledge and research in the topic of time series with econometric and other applications.

A survey of recent methodology regarding the statistical analysis and detection of defective items in various types of inspection for attributes, when the inspection itself is subject to error. The book covers both theory and practice, with an emphasis on group testing.

Kernel smoothing has greatly evolved since its inception to become an essential methodology in the Data Science tool kit for the 21st century. Its widespread adoption is due to its fundamental role for multivariate exploratory data analysis, as well as the crucial role it plays in composite solutions to complex data challenges.

Precision medicine seeks to use data to construct principled, i.e., evidence-based, treatment strategies that dictate where, when, and to whom treatment should be applied. This book provides an accessible yet comprehensive introduction to statistical methodology for dynamic treatment regimes.

Statistical methods for sequential hypothesis testing and changepoint detection have applications across many fields. This book presents an overview of methodology in these related areas, providing a synthesis of research from the last few decades.

This monograph deals with a class of statistical models that generalizes classical linear models to include many other models that have been found useful in statistical analysis.

"Business, government, and industry all need efficient and accurate methods of summarizing and extracting information from the huge amounts of data collected and stored electronically. Thoroughly updated, the second edition of this popular text presents graphical and clustering methods.

Hidden Markov Models (HMMs) remains a vibrant area of research in statistics, with many new applications appearing since publication of the first edition.

A variety of power tools for data analysis, based on non-parametric regression or smoothing techniques, are described in this text. These methods relax the usual linear assumption in many standard models, allowing the analyst to uncover structure in data.

Originally published in 1990, Subset Selection in Regression filled a gap in the literature, and its critical and popular success endured for more than a decade. The second edition continues that tradition and remains dedicated to the techniques for fitting and choosing models that are linear in their parameters and to understanding and correcting the bias introduced by selecting a model. The author thoroughly updated each chapter, added material that reflects recent developments in theory and methods, and included more examples and references. The presentation is clear, concise, and as the Journal of the American Statistical Association reported about the first edition, goes "straight to the guts of a complex problem."

This is the second edition of a monograph on generalized linear models with random effects that extends the classic work of McCullagh and Nelder. It has been thoroughly updated, with around 80 pages added, including new material on the extended likelihood approach that strengthens the theoretical basis of the methodology, new developments in variable selection and multiple testing, and new examples and applications. It includes an R package for all the methods and examples that supplement the book.

This book provides broad, up-to-date coverage of the Pareto model and its extensions. This edition expands several chapters to accommodate recent results and reflect the increased use of more computer-intensive inference procedures. It includes new material on multivariate inequality and new discussions of bivariate and multivariate income and survival models. This edition also explores recent ways of handling the problems of inference for Pareto models and their generalizations and extensions.

More than twice the size of its predecessor, this second edition reflects the major growth in spatial statistics as both a research area and an area of application. This edition includes four new chapters on spatial point patterns, big data, spatial and spatiotemporal gradient modeling, and the theoretical aspects of point-referenced modeling. It also expands several other chapters, updates the WinBUGS programs and R packages, doubles the number of exercises, and integrates many more color figures throughout the text.

This third edition contains new chapters on re-estimating sample size when testing for average bioequivalence, fitting a nonlinear dose response function, estimating a dose to take forward from phase two to phase three, establishing proof of concept, and recalculating the sample size using conditional power. It employs the specially created R package Crossover, includes updates regarding period baselines and data analysis from very small trials, reflects the availability of new procedures in SAS, and presents proc mcmc as an alternative to WinBUGS for Bayesian analysis.

Presenting an extensive set of tools and methods for data analysis, this second edition includes more models and methods and significantly extends the possible analyses based on ranks. It contains a new section on rank procedures for nonlinear models, a new chapter on models with dependent error structure, and new material on the development of computationally efficient affine invariant/equivariant sign methods based on transform-retransform techniques in multivariate models. The authors illustrate the methods using many real-world examples and R. Information about the data sets and R packages can be found at www.crcpress.com

Retaining all the material from the second edition and adding substantial new material, this third edition presents models and statistical methods for analyzing spatially referenced point process data. Reflected in the title, this edition now covers spatio-temporal point patterns. It also incorporates the use of R through several packages dedicated to the analysis of spatial point process data, with code and data sets available online. Practical examples illustrate how the methods are applied to analyze spatial data in the life sciences.

Intended for academic statisticians, this book text such topics as: exact methods, with permutation techniques as the main unifying theme; estimating equations; and asymptotic approximations, particularly in the estimation of parameters in a general linear model.

Deals with the techniques for fitting and choosing models that are linear in their parameters and to understanding and correcting the bias introduced by selecting a model. This title includes a chapter on Bayesian methods and an example from the field of near infrared spectroscopy. It emphasises on cross-validation and focuses on bootstrapping.

Offers an overview of analysis strategies for regression models in which variables are measured with errors. This book includes material on Bayesian methods and semiparametric regression and a chapter on generalized linear mixed models.