Statistical Analysis of Stochastic Processes in Time
Statistical Analysis of Stochastic Processes in Time
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.
- Covers stochastic processes needed by research scientists and students with many practical examples and exercises
- Treats applications in a wide variety of disciplines
- R code, data sets and worked examples provided free on author's website
Reviews & endorsements
'This book is an extraordinary piece of literature … It is simply a masterpiece and even the most experienced statistician will learn a thing or two from this text. … It is more than a mere cookery book type of statistical commands, output and interpretation but, rather, a deep understanding and appreciation of the 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, Series A
'… the book fills a gap between the more fundamental, topical volumes around, and more popular texts on these matters. it is very well readable, and it provides both an excellent introduction and a good overview over much of stochastic methods applicable in longitudinal data.' Environmental and Ecological Statistics
Product details
October 2004Hardback
9780521837415
354 pages
254 × 178 × 21 mm
0.83kg
Available
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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.
