Statistical analysis of stochastic processes in time / / J.K. Lindsey [[electronic resource]]

• Record Nr.: UNINA9910784307703321
• Autore: Lindsey James K.
• Pubbl/distr/stampa: Cambridge : , : Cambridge University Press, , 2004
• ISBN: 1-107-16168-1; 1-280-54056-7; 9786610540563; 0-511-21552-5; 0-511-21731-5; 0-511-21194-5; 0-511-31590-2; 0-511-61716-X; 0-511-21371-9
• Descrizione fisica: 1 online resource (xiv, 338 pages) : digital, PDF file(s)
• Collana: Cambridge series on statistical and probabilistic mathematics ; ; 14
• Disciplina: 519.2/3
• Soggetti: Stochastic processes
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Title from publisher's bibliographic system (viewed on 05 Oct 2015).
• Nota di bibliografia: Includes bibliographical references and indexes.
• Nota di contenuto: 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
• Sommario/riassunto: 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.