Books in the Chapman & Hall/CRC Texts in Statistical Science series

This publication provides an insight into modern developments in statistical methodology using examples that highlight connections between these techniques as well as their relationship to other established approaches. Illustration by simple numerical examples takes priority over abstract theory.

This textbook and reference book is aimed at statisticians and scientists who would like to gain practical experience with the design and analysis of experiments, with enough theory to understand the analysis of standard and non-standard experimental design.

Intended for those involved in complex processes in any industry, this book covers basic and more advanced techniques of data analysis. It also discusses experimental design and the so-called "Taguchi methods". Throughout the emphasis is on quality improvement and process capability.

Focusing on the important role that statistical methods play in the analysis of the data collected as well as in the overall clinical trial process, this title provides an introduction to clinical trials. It features examples, exercises, and material on binary outcomes and survival analysis. It features various real examples taken from The Lancet.

Incorporating changes in theory and highlighting various applications, this book presents a comprehensive introduction to the methods of Markov Chain Monte Carlo (MCMC) simulation technique. It incorporates the developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection.

A practical guide to statistical methods which reflects developments in the field. It includes a chapter introducing mixed models for binary data analysis and another on methods for modelling binary data. It also includes material on modelling ordered categorical data and provides a summary of the leading software packages.

Linear algebra and the study of matrix algorithms have become fundamental to the development of statistical models. Using a vector space approach, this book provides an understanding of the major concepts that underlie linear algebra and matrix analysis.

Like its widely praised, best-selling predecessor, this second edition explains how to use linear models in physical science, engineering, social science, and business applications. The material on interpreting linear models now distinguishes the main applications of prediction and explanation and introduces elementary notions of causality. This edition also covers QR decomposition, splines, additive models, Lasso, multiple imputation, and false discovery rates. It extensively uses R¿s ggplot2 graphics package in addition to base graphics.

This book clarifies, in an approachable and practical way, the general principles involved in tackling real-life statistical problems.

This is a first year graduate text on large sample theory in statistics. Nearly all topics are covered in their multivariate settings. The text falls into four parts and includes many examples. In the first part, basic probabilistic notions are treated.

Covers both contemporary and classical aspects of statistics, including survival analysis, Kernel density estimation, Markov chain Monte Carlo, hypothesis testing, regression, bootstrap, and generalised linear models. This work provides the option of using powerful computational tools for stochastic modelling.

Reflects the developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets. This title summarizes relevant general statistical concepts, introduces basic ideas of nonparametric or distribution-free methods, and features factorial treatment structures.

An introduction to statistics for technology, presenting the range of statistical methods commonly used in science, social science and engineering. The mathematics is simple and straightforward; statistical concepts are explained carefully; and real-life examples are used throughout the book.

This book takes a first step in developing a full theory of richly parameterized models, which would allow statisticians to better understand their analysis results.

This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis.

This text presents a balanced account of the Bayesian and frequentist approaches to statistical inference. Along with more examples and exercises, this second edition includes new material on empirical Bayes and penalized likelihoods and their impact on regression models and offers expanded material on hypothesis testing, method of moments, bias correction, and hierarchical models. It also compares the Bayesian and frequentist schools of thought and explores procedures that lie on the border between the two.

A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily. Along with covering traditional methods, this book describes many recent SPC methods that improve upon the more established techniques. The author¿a leading researcher on SPC¿shows how these methods can handle new applications. Pseudo codes are presented for important methods and all R functions and datasets are available on the author¿s website.

This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis.

Assumes one-semester of calculus. "Stories" make distributions (Normal, Binomial, Poisson that are widely-used in statistics) easier to remember, understand. Many books write down formulas without explaining clearly why these particular distributions are important or how they are all connected.