Synopses & Reviews
This introduction to ways of modelling a wide variety of phenomena that occur over time is accessible to anyone with a basic knowledge of statistical ideas. J.K. Lindsey concentrates on tractable models involving simple processes for which explicit probability models, hence likelihood functions, can be specified. (These models are the most useful in statistical applications modelling empirical data.) Examples are drawn from physical, biological and social sciences, to show how the book's underlying ideas can be applied, and data sets and R code are supplied for them.
Review
"Well-written, enjoyable."
Technometrics"This book is an extraordinary piece of literature which gives the non-fluent statistician the ability to model random events. It is simply a masterpiece and even the most experienced statistician will learn a thing or two from this text...Examples in this text not only use real data but also carry the reader through the entire statistical thinking process...The book is well written and would be good reading for applied statisticians as well as all post-graduate and faculty members who interact with data. Libraries should purchase a copy."
Journal of the Royal Statistical Society
Synopsis
Many observed phenomena are characterised by quantities that vary over time: stochastic processes are designed to study them. This book introduces practical methods for applying stochastic processes. 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. 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.
Table of Contents
Preface; Part I. Basic Principles: 1. What is a stochastic process?; 2. Normal theory models and extensions; Part II. Categorical State Space: 3. Survival processes; 4. Recurrent events; 5. Discrete-time Markov chains; 6. Event histories; 7. Dynamics models; 8. More complex dependencies; Part III. Continuous State Space: 9. Time series; 10. Growth curves; 11. Dynamic models; 12. Repeated measurements; Bibliography; Author index; Subject index.