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Statistical Analysis of Stochastic P...

Ebooks Corporation.

Statistical Analysis of Stochastic Processes in Time.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical Analysis of Stochastic Processes in Time.
Author:	Lindsey, J. K.
other author:	Gill, R.
Published:	Cambridge :Cambridge University Press,2004.
Description:	354 p.
Subject:	Stochastic processes.
Online resource:	Click here to view book
ISBN:	9780511617164 (electronic bk.)

Statistical Analysis of Stochastic Processes in Time.
Lindsey, J. K.

Statistical Analysis of Stochastic Processes in Time.[electronic resource]. - Cambridge :Cambridge University Press,2004. - 354 p.

Cover; Half-title; Series-title; Title; Copyright; Contents; Preface; Notation and symbols; 1 What is a stochastic process?; 2 Basics of statistical modelling; 3 Survival processes; 4 Recurrent events; 5 Discrete-time Markov chains; 6 Event histories; 7 Dynamic models; 8 More complex dependencies; 9 Time series; 10 Diffusion and volatility; 11 Dynamic models; 12 Growth curves; 13 Compartment models; 14 Repeated measurements; References; Author index; Subject index

This introduction to ways of modelling phenomena that occur over time is accessible to anyone with a basic knowledge of statistical ideas. Examples from physical, biological and social sciences show how the principles can be put into practice: data sets and R code for these are supplied on author's website.

Electronic reproduction.

Available via World Wide Web.

Mode of access: World Wide Web.

ISBN: 9780511617164 (electronic bk.)Subjects--Topical Terms:

181874
Stochastic processes.
Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: QA274 .L54 2004eb

Dewey Class. No.: 519.23

Statistical Analysis of Stochastic Processes in Time.
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505 0 $a Cover; Half-title; Series-title; Title; Copyright; Contents; Preface; Notation and symbols; 1 What is a stochastic process?; 2 Basics of statistical modelling; 3 Survival processes; 4 Recurrent events; 5 Discrete-time Markov chains; 6 Event histories; 7 Dynamic models; 8 More complex dependencies; 9 Time series; 10 Diffusion and volatility; 11 Dynamic models; 12 Growth curves; 13 Compartment models; 14 Repeated measurements; References; Author index; Subject index
520 $a This introduction to ways of modelling phenomena that occur over time is accessible to anyone with a basic knowledge of statistical ideas. Examples from physical, biological and social sciences show how the principles can be put into practice: data sets and R code for these are supplied on author's website.
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