A semiparametric approach to hidden Markov models under longitudinal observations
2009 (English)In: Statistics and computing, ISSN 0960-3174, E-ISSN 1573-1375, Vol. 19, no 4, 381-393 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2009. Vol. 19, no 4, 381-393 p.
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:kth:diva-61177DOI: 10.1007/s11222-008-9099-2ISI: 000271738000003OAI: oai:DiVA.org:kth-61177DiVA: diva2:478672
QC 201202242012-01-162012-01-162012-02-24Bibliographically approved