A Model for Overdispersion and Underdispersion using Latent Markov Processes

Jeeraporn Thaithanan; Sujit K. Ghosh; Chinnaphong Bumrungsup

A Model for Overdispersion and Underdispersion using Latent Markov Processes

Authors

Jeeraporn Thaithanan Department of Mathematics and Statistics, Faculty of Science and Technology, Thammasat University, Pathum Thani 12120, Thailand.
Sujit K. Ghosh Department of Statistics, NC State University, Raleigh, NC 27695-8203, USA.
Chinnaphong Bumrungsup Department of Mathematics and Statistics, Faculty of Science and Technology, Thammasat University, Pathum Thani 12120, Thailand.

Keywords:

Bayesian Method, Markov Processes, Monte Carlo Simulation, Overdispersion, Underdispersion, Zero-Altered Distribution

Abstract

A new model for both overdispersion and underdispersion using latent Markov processes modeled a stationary processes is proposed. The parameters in this model can be estimated by the Bayesian method. The performance of the proposed method for the new model, evaluating in term of bias, MSE and coverage probability, has been explored using numerical methods based on simulated and real data.

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How to Cite

Thaithanan, J., Ghosh, S. K., & Bumrungsup, C. (2015). A Model for Overdispersion and Underdispersion using Latent Markov Processes. Thailand Statistician, 10(2), 183–197. Retrieved from https://ph02.tci-thaijo.org/index.php/thaistat/article/view/34226