Hidden Markov models for time series: an introduction using R

✍ Scribed by Langrock, Roland; MacDonald, Iain L.; Zucchini, W

Publisher: CRC Press
Year: 2016
Tongue: English
Leaves: 388
Series: Monographs on statistics and applied probability (Series) 150
Edition: Second edition
Category: Library

No coin nor oath required. For personal study only.

✦ Table of Contents

Content: Preface
Preface to rst edition
Notation and abbreviations
Part I: Model structure, properties and methods
Chapter 1: Preliminaries: mixtures and Markov chains
Chapter 2: Hidden Markov models: de nition and properties
Chapter 3: Estimation by direct maximization of the likelihood
Chapter 4: Estimation by the EM algorithm
Chapter 5: Forecasting, decoding and state prediction
Chapter 6: Model selection and checking
Chapter 7: Bayesian inference for Poisson{hidden Markov models
Chapter 8: R packages
Part II: Extensions Chapter 9: HMMs with general state-dependent distributionChapter 10: Covariates and other extra dependencies
Chapter 11: Continuous-valued state processes
Chapter 12: Hidden semi-Markov models and their representation as HMMs
Chapter 13: HMMs for longitudinal data
Part III: Applications
Chapter 14: Introduction to applications
Chapter 15: Epileptic seizures
Chapter 16: Daily rainfall occurrence
Chapter 17: Eruptions of the Old Faithful geyser
Chapter 18: HMMs for animal movement
Chapter 19: Wind direction at Koeberg
Chapter 20: Models for nancial series Chapter 21: Births at Edendale HospitalChapter 22: Homicides and suicides in Cape Town, 1986{1991
Chapter 23: A model for animal behaviour which incorporates feedback
Chapter 24: Estimating the survival rates of Soay sheep from mark{recapture{recovery data
Appendix A: Examples of R code
Appendix B: Some proofs
References

✦ Subjects

Time-series analysis.;Markov processes.;R (Computer program language);MATHEMATICS / Applied;MATHEMATICS / Probability & Statistics / General;MATHEMATICS / Applied.;MATHEMATICS / Probability & Statistics / General.

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