The University of Southampton
University of Southampton Institutional Repository

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).

Record type: Article

Abstract

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.

Full text not available from this repository.

More information

Published date: December 2011
Organisations: Statistics, Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 341228
URI: http://eprints.soton.ac.uk/id/eprint/341228
ISSN: 0306-7734
PURE UUID: aa982ecb-5301-4be5-befc-5e92879aabe3

Catalogue record

Date deposited: 18 Jul 2012 14:14
Last modified: 18 Jul 2017 05:37

Export record

Altmetrics

Contributors

Author: Antonello Maruotti

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×