J. K. Lindsey – författare

This second edition of Models for Repeated Measurements has been comprehensively revised and updated, taking into account the huge amount of research that has been carried out in the subject in recent years. A wide variety of useful new models is now available, models that have revolutionized the analysis of such data. The second edition contains three new chapters on models for continuous non-normal data, on various design issues specific to repeated measurements, and on missing data and dropouts. Exercises have been added at the ends of most chapters, and the programming functions needed to carry out the analyses in the book are publicly available. Models for Repeated Measurements is an essential reference for research statisticians in agriculture, medicine, economics, and psychology, and for the many consulting statisticians who want an up-to-date expository account of this important topic. The book is organized into four parts. In the first part, the general context of repeated measurements is presented. The three basic types of response variables, continuous (normal), categorical and count, and duration, are introduced. There is a discussion of the ways in which such repeated observations are interdependent. The book also develops a framework for constructing suitable models, with the introduction of the necessary concepts of multivariate distributions and stochastic processes. In the following three parts, a large number of specific examples, including data tables, is presented to illustrate the models available. Each of these parts corresponds to one of the types of responses mentioned above. FROM REVIEWS OF THE FIRST EDITION . . . a timely book . . . useful both as a graduate text and as a consulting source. Statistical Methods in Medical Research

Categorical data analysis is a special area of generalised linear models, which has become the most important area of statistical applications in many disciplines, from medicine to social sciences. This text presents the standard models and many newly developed ones in a language which can be immediately applied in many modern statistical packages such as GLIM, GENSTAT, S-Plus, as well as SAS and LISP-STAT. The book is structure around the distinction between independent events occurring to different individuals, resulting in frequencies, and repeated events occurring to the same individuals, yielding counts. The book demonstates that much of modern statistics can be seen as special cases of categorical data models; both generalized linear models and proportional hazards models can be fitted as log linear models. More specialized topics such as Markov chains, overdispersion and random effects, are also covered.

Inference involves drawing conclusions about some general phenomenon from limited empirical observations in the face of random variability. Two central unifying components of statistics are the likelihood function and the exponential family. These are here brought together for the first time as the central themes of a book on statistical inference. This book is appropriate as an advanced undergraduate or graduate text in mathematical statistics.

This text is aimed at students in medicine, biology, and the social sciences, as well as those planning to specialise in applied statistics. It covers the basics of the design and analysis of surveys and experiments and provides an understanding of the basic principles of modelling and inference. Practical advice is provided on how to design a study, collect data, record observations accurately, detect errors, construct appropriate models, and interpret the results. The text contains many illustrative examples and exercises relating statistical principles to research.A companion website is available with links to data sets, R codes, and to an instructor's manual with teaching hints and solutions.

This text is aimed at students in medicine, biology, and the social sciences, as well as those planning to specialise in applied statistics. It covers the basics of the design and analysis of surveys and experiments and provides an understanding of the basic principles of modelling and inference. Practical advice is provided on how to design a study, collect data, record observations accurately, detect errors, construct appropriate models, and interpret the results. The text contains many illustrative examples and exercises relating statistical principles to research. A companion website is available with links to data sets, R codes, and to an instructor's manual with teaching hints and solutions.

This book was first published in 2004. Many observed phenomena, from the changing health of a patient to values on the stock market, are characterised by quantities that vary over time: stochastic processes are designed to study them. This book introduces practical methods of applying stochastic processes to an audience knowledgeable only in basic statistics. It covers almost all aspects of the subject and presents the theory in an easily accessible form that is highlighted by application to many examples. These examples arise from dozens of areas, from sociology through medicine to engineering. Complementing these are exercise sets making the book suited for introductory courses in stochastic processes. Software (available from www.cambridge.org) is provided for the freely available R system for the reader to apply to all the models presented.