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Statistical analysis of stochastic processes in time /
"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 knowledgeab...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, UK ; New York :
Cambridge University Press,
2004.
|
| Colección: | Cambridge series on statistical and probabilistic mathematics ;
14. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Lindsey, James K., |e author. | |
| 245 | 1 | 0 | |a Statistical analysis of stochastic processes in time / |c J.K. Lindsey. |
| 264 | 1 | |a Cambridge, UK ; |a New York : |b Cambridge University Press, |c 2004. | |
| 300 | |a 1 online resource (xiv, 338 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a Cambridge series in statistical and probabilistic mathematics ; |v 14 | |
| 504 | |a Includes bibliographical references and indexes. | ||
| 588 | 0 | |a Print version record. | |
| 520 | |a "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."--Provided by publisher | ||
| 505 | 0 | |a Cover -- Half-title -- Series-title -- Title -- Copyright -- Contents -- Preface -- Notation and symbols -- Part I Basic principles -- 1 What is a stochastic process? -- 1.1 Definition -- 1.1.1 Time -- 1.1.2 State space -- 1.1.3 Randomness -- 1.1.4 Stationarity, equilibrium, and ergodicity -- Multivariate distributions -- Stationarity -- Equilibrium -- Ergodicity -- Regeneration points -- 1.1.5 Replications -- 1.2 Dependence among states -- 1.2.1 Constructing multivariate distributions -- 1.2.2 Markov processes -- 1.2.3 State dependence -- 1.2.4 Serial dependence -- 1.2.5 Birth processes. | |
| 505 | 8 | |a 1.3 Selecting models -- 1.3.1 Preliminary questions -- 1.3.2 Inference -- Further reading -- Exercises -- 2 Basics of statistical modelling -- 2.1 Descriptive statistics -- 2.1.1 Summary statistics -- 2.1.2 Graphics -- 2.2 Linear regression -- 2.2.1 Assumptions -- 2.2.2 Fitting regression lines -- Likelihood -- Multiple regression -- Interactions -- 2.3 Categorical covariates -- 2.3.1 Analysis of variance -- Baseline constraint -- Mean constraint -- 2.3.2 Analysis of covariance -- Interactions -- 2.4 Relaxing the assumptions -- 2.4.1 Generalised linear models -- Gamma distribution. | |
| 505 | 8 | |a Log normal and inverse Gauss distributions -- 2.4.2 Other distributions -- Weibull distribution -- Other distributions -- 2.4.3 Nonlinear regression functions -- Logistic growth curve -- Further reading -- Exercises -- Part II Categorical state space -- 3 Survival processes -- 3.1 Theory -- 3.1.1 Special characteristics of duration data -- Interevent times -- Intensity of events -- Absorbing states -- Time origin -- 3.1.2 Incomplete data -- Censoring -- Stopping rules -- Time alignment -- 3.1.3 Survivor and intensity functions -- 3.1.4 Likelihood function -- 3.1.5 Kaplan-Meier curves. | |
| 505 | 8 | |a 3.2 Right censoring -- 3.2.1 Families of models -- Proportional hazards -- Accelerated failure times -- 3.2.2 Intensity and survivor functions -- 3.3 Interval censoring -- 3.3.1 Probability models -- 3.4 Finite mixtures -- 3.4.1 Probability models -- 3.5 Models based directly on intensities -- 3.5.1 Durations and counts of events -- 3.6 Changing factors over a lifetime -- 3.6.1 Complex regression function -- 3.6.2 Overdispersion -- Further reading -- Exercises -- 4 Recurrent events -- 4.1 Theory -- 4.1.1 Counting processes -- Basic concepts -- Some simple special cases -- Modelling intensities. | |
| 505 | 8 | |a 4.1.2 Poisson process -- Poisson distribution -- Exponential distribution -- Modifications of Poisson processes -- Nonhomogeneous Poisson processes -- 4.1.3 Departures from randomness -- 4.1.4 Renewal processes -- Asymptotics -- Stationarity -- Recurrence times -- Variability -- Types of failure -- 4.2 Descriptive graphical techniques -- 4.2.1 Detecting trends -- Cumulative events and counts of events -- 4.2.2 Detecting time dependence -- 4.2.3 Kaplan-Meier curves -- 4.3 Counts of recurrent events -- 4.3.1 Poisson regression -- 4.3.2 Over- and underdispersion -- 4.4 Times between recurrent events. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
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| 650 | 0 | |a Stochastic processes. | |
| 650 | 0 | |a Probabilities. | |
| 650 | 2 | |a Stochastic Processes | |
| 650 | 2 | |a Probability | |
| 650 | 6 | |a Processus stochastiques. | |
| 650 | 6 | |a Probabilités. | |
| 650 | 7 | |a probability. |2 aat | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x Stochastic Processes. |2 bisacsh | |
| 650 | 7 | |a Probabilities |2 fast | |
| 650 | 7 | |a Stochastic processes |2 fast | |
| 650 | 7 | |a Processos estocasticos. |2 larpcal | |
| 650 | 7 | |a Estatística. |2 larpcal | |
| 758 | |i has work: |a Statistical analysis of stochastic processes in time (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGFrhTyK4RH7g3dbp74fxC |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Lindsey, James K. |t Statistical analysis of stochastic processes in time. |d Cambridge, UK ; New York : Cambridge University Press, 2004 |w (DLC) 2004045684 |
| 830 | 0 | |a Cambridge series on statistical and probabilistic mathematics ; |v 14. | |
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