Applications of Poisson-Hidden Markov Model

Huang, X.X.; Cheng, J.

Abstract

Some real discrete time-series counts can be considered as the random variables which follow Poisson distribution governed by Hidden Markov Chain. In this paper, a Poisson-Hidden Markov Model is employed to capture the switching between two states in different areas of applications (i.e. economy and biotic environment). The Baum-Welch method and Maximum Likelihood Estimation (MLE) are adopted to estimate parameters. Next, the standard error of parameters can be calculated through the parametric bootstrap technique. In empirical analysis, the Poisson-Hidden Markov Model seems to be validated since it captures the hidden influential factors of bank failures and natural factors on manatees' deaths successfully. It is likely that the Poisson-Hidden Markov Model can be utilized in other research area and help researchers make further analysis.

Citation

Huang, X., & Cheng, J. (2015). Applications of Poisson-Hidden Markov Model. International Journal of Applied Mathematics and Statistics (IJAMAS), 53(4),

• Journal Article Type: Article
• Publication Date: 2015
• Deposit Date: Dec 14, 2023
• Journal: International Journal of Applied Mathematics and Statistics
• Print ISSN: 0973-1377
• Publisher: Centre for Environment & Socio-Economic Research Publications
• Peer Reviewed: Peer Reviewed
• Volume: 53
• Issue: 4
• Keywords: Poisson distribution, Hidden Markov model, the Baum-Welch method, Parametric Bootstrap technique
• Publisher URL: http://www.ceser.in/ceserp/index.php/ijamas/article/view/3674/0