Hidden Markov models for time series : a practical introduction using R /: a practical introduction using R. (2016)

• Record Type: Book
• Further Information: Note: Walter Zucchini, Iain L. MacDonald, Roland Langrock.
• Authors: Zucchini, W; MacDonald, Iain L; Langrock, Roland, 1983-
• Other Names: MacDonald, Iain L
• Contents: Model structure, properties and methods ; Preliminaries: mixtures and Markov chains; Introduction; Independent mixture models; Markov chains; Exercises Hidden Markov models: definition and properties ; A simple hidden Markov model; The basics; The likelihood; Exercises Direct maximization of the likelihood ; Introduction; Scaling the likelihood computation; Maximization subject to constraints; Other problems; Example: earthquakes; Standard errors and confidence intervals; Example: parametric bootstrap; Exercises Estimation by the EM algorithm; Forward and backward probabilities; The EM algorithm; Examples of EM applied to Poisson-HMMs; Discussion; Exercises Forecasting, decoding and state prediction; Conditional distributions; Forecast distributions; Decoding; State prediction; HMMs for classification; Exercises Model selection and checking; Model selection by AIC and BIC; Model checking with pseudo-residuals; Examples; Discussion; Exercises Bayesian inference for Poisson-HMMs; Applying the Gibbs sampler to Poisson-HMMs; Bayesian estimation of the number of states; Example: earthquakes; Discussion; Exercises R packages; The package depmixS4; The package HiddenMarkov; The package msm; The package R20penBUGS ; Discussion Extensions ; General state-dependent distributions; Introduction; Univariate state-dependent distribution; Multinomial and categorical HMMs; Multivariate state-dependent distribution; Exercises Covariates and other extra dependencies; Introduction; HMMs with covariates; HMMs based on a second-order Markox chain; HMMs with other additional dependencies; Exercises Continuous-valued state processes; Introduction; Models with continous-valued state process; Fitting an SSM to the earthquake data; Discussion Hidden semi-Markov models as HMMs; Introduction; Semi-Markov processes, hidden semi-Markov models and approximating HMMs; Examples of HSMMs as HMMs; General HSMM; R code; Some examples of dwell-time distributions; Fitting HSMMs via the HMM representation; Example: earthquakes; Discussion; Exercises HMMs for longitudinal data; Introduction; Some parameters constant across components; Models with random effects; Discussion; Exercises Applications ; Introduction to applications Epileptic seizures; Introduction; Models fitted; Model checking by pseudo-residuals; Exercises Daily rainfall occurrence; Introduction; Models fitted Eruptions of the Old Faithful geyser; Introduction; The data; Binary time series of short and long eruptions; Normal-HMMs for durations and waiting times; Bivariate model for durations and waiting times; Exercises HMMs for animal movement; Introduction; Directional data; HMMs for movement data; Basic HMM for Drosophila movement; HMMs and HSMMs for bison movement; Mixed HMMs for woodpecker movement; Exercises Wind direction at Koeberg; Introduction; Wind direction classified into 16 categories; Wind direction as a circular variable; Exercises Models for financial series; Multivariate HMM for returns on four shares; Stochastic volatility models; Exercises Births at Edendale Hospital; Introduction; Models for the proportion Caesarean; Models for the total number of deliveries; Conclusion Homicides and suicides in Cape Town; Introduction; Firearm homicides as a proportion of all homicides, suicides and legal intervention homicides; The number of firearm homicides; Firearm homicide and suicide proportions; Proportion in each of the five categories Animal behaviour model with feedback; Introduction; The model; Likelihood evaluation; Parameter estimation by maximum likelihood; Model checking; Inferring the underlying state; Models for a heterogeneous group of subjects; Other modifications or extensions; Application to caterpillar feeding behaviour; Discussion Survival rates of Soay sheep; Introduction; MRR data without use of covariates; MRR data involving covariate information; Application to Soay sheep data; Conclusion Examples of R code; The functions; Examples of code using the above functions Some proofs; Factorization needed for forward probabilities; Two results for backward probabilities; Conditional independence of Xt1 and XTt+1 References Author index Subject index
• Edition: Second edition
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2016
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 519.233; Hidden Markov models; Time-series analysis; R (Computer program language)
• Languages: English
• ISBNs: 9781482253849
• Related ISBNs: 9781482253832
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; item not viewed.
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