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A semiparametric approach to hidden Markov models under longitudinal observations

Record type: Article

We propose a hidden Markov model for longitudinal count data where sources of unobserved heterogeneity arise, making data overdispersed. The observed process, conditionally on the hidden states, is assumed to follow an inhomogeneous Poisson kernel, where the unobserved heterogeneity is modeled in a generalized linear model (GLM) framework by adding individual-specific random effects in the link function.

Due to the complexity of the likelihood within the GLM framework, model parameters may be estimated by numerical maximization of the log-likelihood function or by simulation methods; we propose a more flexible approach based on the Expectation Maximization (EM) algorithm. Parameter estimation is carried out using a non-parametric maximum likelihood (NPML) approach in a finite mixture context. Simulation results and two empirical examples are provided.

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Citation

Maruotti, Antonello and Ryden, Tobias (2009) A semiparametric approach to hidden Markov models under longitudinal observations Statistics and Computing, 19, (4), pp. 381-393. (doi:10.1007/s11222-008-9099-2).

More information

e-pub ahead of print date: 27 September 2008
Published date: 2009
Keywords: hidden markov models, longitudinal data, mixed hidden markov models, random effects, npml
Organisations: Statistics, Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 345968
URI: http://eprints.soton.ac.uk/id/eprint/345968
ISSN: 0960-3174
PURE UUID: 55bf4022-29be-4fba-a28b-8bec9eefa9ef

Catalogue record

Date deposited: 10 Dec 2012 14:00
Last modified: 18 Jul 2017 05:06

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Contributors

Author: Antonello Maruotti
Author: Tobias Ryden

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