Mixed hidden Markov models for longitudinal data: an overview
Maruotti, Antonello (2011) Mixed hidden Markov models for longitudinal data: an overview. International Statistical Review, 79, (3), 427-454. (doi:10.1111/j.1751-5823.2011.00160.x).
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In this paper we review statistical methods which combine hidden Markov models (HMMs) and random effects models in a longitudinal setting, leading to the class of so-called mixed HMMs. This class of models has several interesting features. It deals with the dependence of a response variable on covariates, serial dependence, and unobserved heterogeneity in an HMM framework. It exploits the properties of HMMs, such as the relatively simple dependence structure and the efficient computational procedure, and allows one to handle a variety of real-world time-dependent data. We give details of the Expectation-Maximization algorithm for computing the maximum likelihood estimates of model parameters and we illustrate the method with two real applications describing the relationship between patent counts and research and development expenditures, and between stock and market returns via the Capital Asset Pricing Model.
|Subjects:||H Social Sciences > HA Statistics|
|Divisions:||Faculty of Social and Human Sciences > Southampton Statistical Sciences Research Institute
Faculty of Social and Human Sciences > Mathematics > Statistics
|Date Deposited:||18 Jul 2012 14:14|
|Last Modified:||06 Dec 2012 15:41|
|Contributors:||Maruotti, Antonello (Author)
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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