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  3. Statistical Analysis of Stochastic Processes in Time
  • Cited by 47
Publisher:
Cambridge University Press
Online publication date:
February 2010
Print publication year:
2004
Online ISBN:
9780511617164
DOI:
https://doi.org/10.1017/CBO9780511617164
Series:
Cambridge Series in Statistical and Probabilistic Mathematics (14)
Subjects:
General Statistics and Probability, Statistical Theory and Methods, Statistics and Probability
Information

Book description

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.

Reviews

'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.'

Source: 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.'

Source: Environmental and Ecological Statistics

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