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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.

This is a revised analysis in which the aspect of primary concern takes one of just two possible forms - success, failure; survives, dies; correct, false; nondefective, defective etc. Such data are called binary methods and it studies how the probability of success depends on explanatory features.

A monograph aimed at providing a delineation of currently available modelling approaches and inferential methods for nonlinear repeated measures, whilst making the material accessible to a wide audience.

Presents the principles of stereology from a statistical viewpoint, focusing on both basic theory and practical implications. This title discusses ways to effectively communicate statistical issues to clients, draws attention to common methodological errors, and provides references to essential literature.

Discusses the application of differential geometry to statistics. The book commences with the simplest differential manifolds - affine spaces and their relevance to exponential families - and passes into the general theory, the Fisher information metric, the Amari connection and asymptotics.

..."by far the best book on its topic." -International Statistical Reveiw

To date, Mixed Poisson processes have been studied by scientists primarily interested in either insurance mathematics or point processes

This book is the first single source volume to fully address this prevalent practice in both its analytical and modeling aspects

An exposition of density estimation for statistics and data analysis. A volume in the "Monographs on Statistics and Applied Probability" series, it is designed for applied statisticians

Covers the diverse statistical methods and applications for data with long-range dependence. The book provides an overview of probabilistic foundations, statistical methods, and applications, emphasizing basic principles and practical applications.

A review of the major statistical techniques which can be used to analyze regression data with nonconstant variability and skewness. The authors have developed techniques to deal with these types of problems, the complications of which can be observed in diverse fields. Annotation copyright Book New

Gaussian Markov Random Field (GMRF) models, most widely used in spatial statistics are presented in this, the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects.

This text describes methods for the analysis of relationships between a set a variables, with emphasis largely, but not entirely on observational studies in the social sciences.

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.

This gives a comprehensive introduction to the (standard) statistical analysis based on the theory of martingales and develops entropy methods in order to treat dependent data in the framework of martingales. The author starts a summary of the martingale theory, and then proceeds to give full proofs of the martingale central limit theorems.

Research on mixed models has been extensive over the most recent decade. This book differs from the authors' previous monographs on longitudinal data in that it focuses on mixed models of a linear, generalized linear and nonlinear type. The book pays attention to recent developments that include diagnostics, semi-parametric methodology, non-normal random effects, multivariate longitudinal data, high-dimensional outcomes, joint modeling of longitudinal and survival data and discrimination and classification based on longitudinal data using mixed models.

Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions between states over time. It is targeted at researchers in medical statistics, epidemiology, demography, and social statistics.

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.