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), pp. 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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1111/j.1751-5823.2011.00160.x
ISSNs: 0306-7734 (print)
Related URLs:
Organisations: Statistics, Statistical Sciences Research Institute
ePrint ID: 341228
Date :
Date Event
December 2011Published
Date Deposited: 18 Jul 2012 14:14
Last Modified: 17 Apr 2017 16:48
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/341228

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