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A comprehensive treatment of the theory of statistical modelling in R with an emphasis on applications to practical problems and an expanded discussion of statistical theory.

This text focuses on optimum experimental design using SAS, a powerful software package that provides a complete set of statistical tools including analysis of variance, regression, categorical data analysis, and multivariate analysis. SAS codes, results, plots, numerous figures and tables are provided, along with a fully supported website.

A reference text in the area of biostatistics (medical statistics). It discusses fully parametric models for discrete repeated measures data. It explores statistical models for time-dependent predictors where there may be feedback between the predictor and response variables.

This book describes statistical techniques for the design and evaluation of research studies on medical diagnostic tests, screening tests, biomarkers and new technologies for classification and prediction in medicine.

A comprehensive account of linear regression models with random coefficients emphasising modelling and computational aspects.

This new edition updates Durbin & Koopman's important text on the state space approach to time series analysis providing a more comprehensive treatment, including the filtering of nonlinear and non-Gaussian series. The book provides an excellent source for the development of practical courses on time series analysis.

Bringing together recent advances in smoothing and semiparametric regression from a Bayesian perspective, this book demonstrates, with worked examples, the application of these statistical methods to a variety of fields including forestry, development economics, medicine and marketing.

This book provides an introduction to the use of nonlinear modelling in medical statistics, including worked through examples in most areas where such techniques are used. It is suitable for both professional and academic statisticians working in medical research. The data and computer code for the examples will be available on the authors web site.

This text brings together important ideas on the model-based approach to sample survey, which has been developed over the last twenty years. Suitable for graduate students and professional statisticians, it moves from basic ideas fundamental to sampling to more rigorous mathematical modelling and data analysis and includes exercises and solutions.

An introduction and guide to using diagnostics as a practical part of regression analysis.

This book provides a comprehensive study of nonlinear estimating equations and artificial likelihoods for statistical inference. It includes a variety of examples from practical applications and is ideal for research statisticians and advanced graduate students.

Originating from a meeting celebrating the 80th birthday of Sir David Cox, this book presents a collection of chapters by major statistical researchers on current developments across a wide range of research areas from epidemiology, environmental science, finance, computing and medicine.

A summary of research into the use of symbolic computation applied to statistical inference problems, showing the potential of the subject to automate statistical calculation. The text begins with algorithms for undergraduate problems and moves on to more powerful tools and applications.

This gives practical and extensive coverage of Reliability Data Analysis using real realiability data to illustrate the statistical methods. Survival analysis, growth models, dependency and systems behaviour are covered, with much background to assist the reader.

This book is an introductory account of time-series analysis, written from the perspective of an applied statistician with a particular interest in biological applications. Separate chapters cover exploratory methods, the theory of stationary random processes, spectral analysis, repeated measurements, ARIMA modelling, forecasting, and bivariate time-series analysis.

This text explains the ideas behind the saddlepoint approximations as well as giving a detailed mathematical description of the subject. Some of the subjects treated are uniformity of the approximations, tests in exponential families and compound sums with applications in insurance mathematics.

This text is a systematic overview of Procrustean methods in one volume, presenting a unifying Analysis of Variance framework for different matching methods and the development of statistical tests.

This text presents the general context of repeated measurements, a large number of concrete examples, including data tables to illustrate the models available, and provides an updated bibliography of the repeated measurements literature.

Categorical data analysis is a special area of generalized linear models, which offers statistical applications in many disciplines. This text presents the models in a language which can be immediately applied in many modern statistical packages, such as GLIM, GENSTAT and S-Plus.

Describes the use of smoothing techniques in statistics, with an emphasis on applications. This book also makes extensive reference to S-Plus, as a computing environment in which examples can be explored. It provides S-Plus functions and example scripts to implement many of the techniques described.

Ideal for graduates and research students in applied statistics and a wide range of quantitative disciplines, this text gives a comprehensive treatment of the theory of statistical modelling with generalised linear models with an emphasis on applications of the theory to practical problems, using the widely available package GLIM4.

Comprises a collection of papers on the subject of estimating functions, designed to provide statisticians with an overview of both theory and applications in biostatistics, stochastic processes and survey sampling.

This book provides the first comprehensive and authoritative exposition of the theory of graphical models. It is primarily concerned with the fundamental mathematical and statistical theory and is based largely on a traditional statistical approach.

A well-designed experiment is an efficient learning resource. Because experiments in the field and in the laboratory cannot avoid random error, statistical methods are essential for their efficient design and analysis. This book presents the fundamentals of optimum experimental design theory.

Multivariate analysis is necessary whenever more than one characteristic is observed on each individual under study. Applications arise in very many areas of study. This book provides a comprehensive introduction to available techniques for analysing date of this form. This edition includes a survey of developments in the subject.

Likelihood methods play a central role in statistical theory and methodology. Recently, a new approach to likelihood inference has been developed that often leads to substantial improvements over classical methods. This book gives a detailed introduction to this modern theory of likelihood methods.

This study describes the validity of models involving statistical distribution of data. The smooth tests are a subset of these tests which are easy to apply and can be used in any situation in which there are relatively large sample sizes.

The analysis of time series data has for many years been a central component of statistical research and practice. Although the theory of linear time series is now well established, that of non-linear time series is still a rapidly developing subject. This book, now available in paperback, is an introduction to some of these developments and the present state of research.

This book explains the statistical theory behind a range of regression problems in which one set of variables is predicted from another. The applications are from industry and medicine where the researchers are using sophisticated electronic measuring devices that are capable of monitoring very many variables.

This text is designed to introduce graduate students and researchers to the primary methods useful for approximating integrals. The emphasis is on those methods that have been found to be of practical use, with the focus on approximating higher-dimensional integrals although lower is also covered.