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Statistical Analysis of Stochastic Processes in Time

Handbook / Manual

Series: Cambridge Series in Statistical and Probabilistic Mathematics Volume: 14

By: JK Lindsey

Cambridge University Press

Hardback | Oct 2004 | #156830 | ISBN: 0521837413
Availability: Usually dispatched within 6 days Details
NHBS Price: £71.99 $93/€79 approx

About this book

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. Much theoretical work has been done but virtually no modern books are available to show how the results can be applied. This book fills that gap by introducing 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 is provided for the freely available R system for the reader to apply to all the models presented.

'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


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.

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