Bayesian Inference for the Discrete Weibull Regression Model with Type-I Right Censored Data

Authors

  • Dusit Chaiprasithikul Department of Mathematics and Statistics, Faculty of Science and Technology, Thammasat University, Pathumthani, Thailand
  • Monthira Duangsaphon Department of Mathematics and Statistics, Faculty of Science and Technology, Thammasat University, Pathumthani, Thailand

Keywords:

Bayesian estimation, random walk Metropolis algorithm, discrete Weibull regression, type-I right censored, over-dispersion

Abstract

This study purposes the use of Bayesian estimation for the discrete Weibull regression under type-I right censored data. Moreover, we compared the performance of the maximum likelihood estimation and the Bayesian estimation with uniform noninformative priors and informative priors using the random walk Metropolis algorithm. A simulation study was conducted to compare the performance of three different estimation methods using mean square error with three types of data: excessive zeros data, under-dispersion data, and over-dispersion data. A real dataset is analyzed to see how the model works in practice. The results from both the simulation study and a real data application showed that the maximum likelihood estimation and the Bayesian estimation with informative priors are both appropriate for the discrete Weibull regression under type-I right censored data in the cases of excessive zeros and under-dispersion. However, the Bayesian estimation with informative priors is more appropriate for the discrete Weibull regression under type-I right censored data than other methods in the case of over-dispersion.

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Published

2022-09-29

How to Cite

Chaiprasithikul, D. ., & Duangsaphon, M. . (2022). Bayesian Inference for the Discrete Weibull Regression Model with Type-I Right Censored Data. Thailand Statistician, 20(4), 791–811. Retrieved from https://ph02.tci-thaijo.org/index.php/thaistat/article/view/247461

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Articles