Bayesian Analysis of Proportions via a Hidden Markov Model

被引：5

作者：

Can, Ceren Eda ^{[1
]}

Ergun, Gul ^{[1
]}

Soyer, Refik ^{[2
]}

机构：

[1] Hacettepe Univ, Dept Stat, TR-06800 Ankara, Turkey

[2] George Washington Univ, Dept Decis Sci, Washington, DC 20052 USA

来源：

METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY | 2022年 / 24卷 / 04期

关键词：

Hidden Markov Model; Proportions; Beta distribution; Gibbs Sampling; Metropolis-Hastings algorithm; BETA REGRESSION-MODELS; TIME-SERIES; LIKELIHOOD; DISPERSION; MIXTURE;

D O I：

10.1007/s11009-022-09971-0

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Time series of proportions arise in many contexts. In this paper, we consider a hidden Markov model (HMM) to describe temporal dependence in such series. In so doing, we introduce a Beta-HMM and develop its Bayesian analysis using Markov Chain Monte Carlo Methods (MCMC). Our proposed model is based on a conjugate prior for beta likelihood which enables us develop Bayesian posterior and predictive computations in an efficient manner. We also address the problem of assessing dimension of the HMM using the marginal likelihood of the model which can be evaluated using posterior samples. Finally, we implement our model and the Bayesian methodology using weekly data on market shares.

引用

页码：3121 / 3139

页数：19