We propose a mixed hidden Markov model for continuous longitudinal data, to the quantile regression perspective. Time-constant and time-varying random parameters are added in the quantile regression model to account for time-invariant and dynamic unobserved factors affecting the variable of interest. A nonparametric maximum likelihood approach is applied to solve the numerical integration problem typically arising in the mixed model framework. Parameter estimates are then obtained by means of an EM algorithm, easily derived by exploiting the forward and backward variables defined in the so called Baum-Welsh recursion.

Mixed hidden Markov models for quantiles / Marino, Maria Francesca; Tzavidis, Nikos. - ELETTRONICO. - (2014), pp. 0-0. (Intervento presentato al convegno 47th Scientific Meeting of the Italian Statistical Society).

Mixed hidden Markov models for quantiles

MARINO, MARIA FRANCESCA;
2014

Abstract

We propose a mixed hidden Markov model for continuous longitudinal data, to the quantile regression perspective. Time-constant and time-varying random parameters are added in the quantile regression model to account for time-invariant and dynamic unobserved factors affecting the variable of interest. A nonparametric maximum likelihood approach is applied to solve the numerical integration problem typically arising in the mixed model framework. Parameter estimates are then obtained by means of an EM algorithm, easily derived by exploiting the forward and backward variables defined in the so called Baum-Welsh recursion.
2014
Proceedings of the 47th Scientific Meeting of the Italian Statistical Society
47th Scientific Meeting of the Italian Statistical Society
Marino, Maria Francesca; Tzavidis, Nikos
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1088968
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