Statistical Analysis of Stochastic Processes in Time

AvJ. K. Lindsey

Inbunden, Engelska, 2004

Del 14 i serien Cambridge Series in Statistical and Probabilistic Mathematics

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2004-08-02
Mått:178 x 254 x 21 mm
Vikt:830 g
Format:Inbunden
Språk:Engelska
Serie:Cambridge Series in Statistical and Probabilistic Mathematics
Antal sidor:354
Förlag:Cambridge University Press
ISBN:9780521837415

Utforska kategorier

Tillämpad matematik inom Naturvetenskap och teknik

Recensioner i media

'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

Innehållsförteckning

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.