Hidden Markov and Other Models for Discrete- valued Time Series: (110 Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Discrete-valued time series are common in practice, but methods for their analysis are not well-known. In recent years, methods have been developed which are specifically designed for the analysis of discrete-valued time series. Hidden Markov and Other Models for Discrete-Valued Time Series introduces a new, versatile, and computationally tractable class of models, the "hidden Markov" models. It presents a detailed account of these models, then applies them to data from a wide range of diverse subject areas, including medicine, climatology, and geophysics. This book will be invaluable to researchers and postgraduate and senior undergraduate students in statistics. Researchers and applied statisticians who analyze time series data in medicine, animal behavior, hydrology, and sociology will also find this information useful.

Table of Contents:
Survey of Models A Survey of Models for Discrete-Valued Time Series Introduction: The Need for Discrete-Valued Time Series Models Markov Chains Higher-Order Markov Chains The DARMA models of Jacobs and Lewis Models Based on Thinning The Bivariate Geometric Models of Block, Langberg, and Stoffer Markov Regression Models Parameter-Driven Models State-Spaced Models Miscellaneous Models Discussion Hidden Markov Models The Basic Models Introduction Some Theoretical Aspects of Hidden Markov Models in Speech Processing Hidden Markov Time Series Models: Definition and Notation Correlation Properties Evaluation of the Likelihood Function Distributional Properties Parameter Estimation Identification of Outliers Reversibility Discussion Extensions and Modifications Introduction Models Based on a Second-Order Markov Chain Multinomial-Hidden Markov Models Multivariate Models Models with State-Dependent Probabilities Depending on Covariates Models in Which the Markov Chain Is Homogeneous but Not Assumed Stationary Models in Which the Markov Chain Is Nonhomogeneous Joint Models for the Numbers of Trials and the Numbers of Successes in Those Trials Discussion Applications Introduction The Durations of Successive Eruptions of the Old Faithful Geyser Epileptic Seizure Counts Births at Edendale Hospital Locomotory Behaviour of Locusta migratoria Wind Direction at Koeberg Evapotranspiration Thinly Traded Shares on the Johannesburt Stock Exchange Daily Rainfall at Durban Homicides and Suicides, Cape Town, 1986-1991 Conclusion Appendices A : Proofs of Results Used in the Derivation of the Baum-Welch Algorithm B: Data

Review :
"The book is well written. It gives a nice description of methods proposed for analysis of discrete-valued time series, their theoretical background and examples of applications. The book can be recommended to statisticians who analyze data as well as to students and teachers. It can be used as very good material for seminars and special lectures." -Zentralblatt f]r Mathematik "the authors should be applauded for providing this concise survey of HMMs and other discrete-valued time series models that have been scattered in the literatureI recommend Hidden Markov and Other Models for Discrete-Valued Time Series to time series analysts who are interested in discrete-valued dependent data." -Journal of the ASA, December 1998