A semiparametric approach to hidden Markov models under longitudinal observations
(2009) In Statistics and Computing 19(4). p.381-393- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1518665
- author
- Maruotti, Antonello and Rydén, Tobias LU
- organization
- publishing date
- 2009
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- NPML, Random effects, Mixed hidden Markov models, Hidden Markov models, Longitudinal data
- in
- Statistics and Computing
- volume
- 19
- issue
- 4
- pages
- 381 - 393
- publisher
- Springer
- external identifiers
-
- wos:000271738000003
- scopus:70450284761
- ISSN
- 0960-3174
- DOI
- 10.1007/s11222-008-9099-2
- language
- English
- LU publication?
- yes
- id
- f0d02331-d838-40d7-bb99-65a033c980e7 (old id 1518665)
- date added to LUP
- 2016-04-01 14:46:15
- date last changed
- 2022-02-19 20:47:00
@article{f0d02331-d838-40d7-bb99-65a033c980e7,
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.}},
author = {{Maruotti, Antonello and Rydén, Tobias}},
issn = {{0960-3174}},
keywords = {{NPML; Random effects; Mixed hidden Markov models; Hidden Markov models; Longitudinal data}},
language = {{eng}},
number = {{4}},
pages = {{381--393}},
publisher = {{Springer}},
series = {{Statistics and Computing}},
title = {{A semiparametric approach to hidden Markov models under longitudinal observations}},
url = {{http://dx.doi.org/10.1007/s11222-008-9099-2}},
doi = {{10.1007/s11222-008-9099-2}},
volume = {{19}},
year = {{2009}},
}