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

"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

"...I envision an audience of masters-and doctoral-level epidemiology and biostatistics students who could benefit from a course from this text. Students with a fear of the technical mathematics of stochastics, but with a need for practical analyses of time-correlated data, may find this text useful before a formal course in stochastics."
Robert Lund, Journal of the American Statistical Association

"The text is aimed at scientists looking for realistic statistical models to help in understanding and explaining the specific conditions of their empirical data."
Quarterly of Applied Mathematics

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