A semiparametric approach to hidden Markov models under longitudinal observations


Maruotti, Antonello and Ryden, Tobias (2008) 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).

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Description/Abstract

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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1007/s11222-008-9099-2
ISSNs: 0960-3174 (print)
Keywords: hidden markov models, longitudinal data, mixed hidden markov models, random effects, npml
Subjects:
Organisations: Statistics, Statistical Sciences Research Institute
ePrint ID: 345968
Date :
Date Event
27 September 2008e-pub ahead of print
2009Published
Date Deposited: 10 Dec 2012 14:00
Last Modified: 17 Apr 2017 16:18
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/345968

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