文章基本信息

标题：Second-order autoregressive Hidden Markov Model
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作者：Daiane Aparecida Zuanetti ; Luis Aparecido Milan
期刊名称：Brazilian Journal of Probability and Statistics
印刷版ISSN：0103-0752
出版年度：2017
卷号：31
期号：3
页码：653-665
DOI：10.1214/16-BJPS328
语种：English
出版社：Brazilian Statistical Association
摘要：We propose an extension of Hidden Markov Model (HMM) to support second-order Markov dependence in the observable random process. We propose a Bayesian method to estimate the parameters of the model and the non-observable sequence of states. We compare and select the best model, including the dependence order and number of states, using model selection criteria like Bayes factor and deviance information criterion (DIC). We apply the procedure to several simulated datasets and verify the good performance of the estimation procedure. Tests with a real dataset show an improved fitting when compared with usual first order HMMs demonstrating the usefulness of the proposed model.