Bayesian Semiparametric Hidden Markov models
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Abstract

In this study, we develop a semiparametric mixed hidden Markov model to
analyze longitudinal data. The proposed model
comprises a parametric transition model for examining how potential predictors influence the probability of transition from one
state to another and a nonparametric
conditional model for revealing the functional effects of explanatory variables on outcomes
of interest. Unlike conventional regression that focuses only on the observation process, the proposed model
simultaneously investigates the observation process and the underlying transition process. Two
correlated random effects, the one is in the conditional model and the other is in the
transition model, are considered to describe the possible
dependency within and/or between the two stochastic processes. We propose a Bayesian approach that combines Bayesian
P-splines and MCMC methods to conduct the statistical analysis.
The empirical performance of the proposed methodology is evaluated via simulation studies. An application to capital
structure choice for Chinese listed companies is presented.

All Author(s) ListKang K, Cai JH, Song XY
Name of Conference22nd International Conference on Computational Statistics
Start Date of Conference23/08/2016
End Date of Conference26/08/2016
Place of ConferenceOviedo
Country/Region of ConferenceSpain
Year2016
LanguagesEnglish-United Kingdom

Last updated on 2018-21-01 at 21:38