Robust fitting of hidden Markov regression models under a longitudinal setting
Robust fitting of hidden Markov regression models under a longitudinal setting
We propose a robust estimation procedure for the analysis of longitudinal data including a hidden process to account for unobserved heterogeneity between subjects in a dynamic fashion. We show how to perform estimation by an expectation–maximization-type algorithm in the hidden Markov regression literature. We show that the proposed robust approaches work comparably to the maximum-likelihood estimator when there are no outliers and the error is normal and outperform it when there are outliers or the error is heavy tailed. A real data application is used to illustrate our proposal. We also provide details on a simple criterion to choose the number of hidden states
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e
23 January 2013
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e
Maruotti, Antonello
(2013)
Robust fitting of hidden Markov regression models under a longitudinal setting.
Journal of Statistical Computation and Simulation.
(doi:10.1080/00949655.2013.763943).
Abstract
We propose a robust estimation procedure for the analysis of longitudinal data including a hidden process to account for unobserved heterogeneity between subjects in a dynamic fashion. We show how to perform estimation by an expectation–maximization-type algorithm in the hidden Markov regression literature. We show that the proposed robust approaches work comparably to the maximum-likelihood estimator when there are no outliers and the error is normal and outperform it when there are outliers or the error is heavy tailed. A real data application is used to illustrate our proposal. We also provide details on a simple criterion to choose the number of hidden states
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Published date: 23 January 2013
Organisations:
Statistical Sciences Research Institute
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Local EPrints ID: 347552
URI: http://eprints.soton.ac.uk/id/eprint/347552
ISSN: 0094-9655
PURE UUID: e767f3ac-0443-40f7-a00f-6c02a2aae4ab
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Date deposited: 24 Jan 2013 08:41
Last modified: 14 Mar 2024 12:50
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Author:
Antonello Maruotti
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