Böcker i Chapman & Hall/CRC Monographs on Statistics and Applied Probability-serien

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.

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

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.

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.

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.

Helps to find the right algorithm for your image processing application. Exploring the achievements that have occurred since the mid-1990s, this title explains how to use modern algorithms to fit geometric contours (circles and circular arcs) to observed data in image processing and computer vision.

The use of simultaneous confidence bands in linear regression is a vibrant area of research. This book presents an overview of the methodology and applications, including necessary background material on linear models.

This text looks at data-analytic approaches to regression problems arising from many scientific disciplines. The aim of these methods is to relax assumptions on the form of a regression function and to let data search for a suitable function. The available data is also described.

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.

Providing a toolkit of analytical methods, this book shows how asymptotics, when coupled with numerical methods, becomes a powerful way to acquire a deeper understanding of the techniques used in probability and statistics.

This book describes the properties of stochastic probabilistic models and develops the applied mathematics of stochastic point processes. It is useful to students and research workers in probability and statistics and also to research workers wishing to apply stochastic point processes.

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.

Bringing together relevant material to impart a clear understanding of how to analyze ROC curves, this book covers the fundamental theory as well as various special topics. It offers illustrative examples of the major methodological developments and includes as much of the mathematical theory as necessary without making the treatment too dense.

The follow-up to the authors' previous book "Asymptotic Techniques", this text gives a systematic account of an important area of theoretical statistics - inference and asymptotics. It should be of interest to researchers in statistics.

Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. Nonparametric regression and density estimation are two of the most fundamental problems to which kernel smoothing provides a simple and effective solution.

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 book presents a radically new approach to problems of evaluating and optimizing the performance of continuous-time stochastic systems

This study of nonparametric regression and generalized linear models contains chapters on approaches to regression, roughness penalties, extensions of the roughness penalty approach, computing the estimates, interpolating and smoothing splines, one-dimensional case, partial splines, and more.

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

Emphasising on MCMC methods, this book explores simulation-based inference for spatial point processes. It examines the Cox and Markov point processes. It provides a treatment of MCMC techniques, particularly those related to statistical inference follows.

Introduces the application of polynomial algebra to experimental design, discrete probability, and statistics. This book offers an introduction to Grobner bases and a description of their applications to experimental design.

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.

Applies empirical likelihood method to problems ranging from those as simple as setting a confidence region for a univariate mean under IID sampling, to problems defined through smooth functions of means, regression models, generalized linear models, estimating equations, or kernel smooths, and to sampling with non-identically distributed data.

Offers a unified Bayesian approach to handle missing data in longitudinal studies. This book contains examples and case studies on aging and HIV. It describes assumptions that include MAR and ignorability, demonstrate the importance of covariance modeling with incomplete data, and cover mixture and selection models for nonignorable missingness.

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.

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.

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.

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.

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.

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.