A Bayesian Estimation and Predictionof Gompertz Extension Distribution Using the MCMC Method

Authors

  • Arun Kumar Chaudhary Faculty of Management Science, Nepal Commerce Campus, Tribhuwan University
  • Vijay Kumar Department of Mathematics and Statistics, DDU Gorakhpur University, Uttar Pradesh

DOI:

https://doi.org/10.3126/njst.v19i1.29795

Keywords:

Bayesian estimation, Gompertz extension distribution, maximum likelihood estimation, markov chain monte carlo, model validation, OpenBUGS

Abstract

In this paper, the Markov chain Monte Carlo (MCMC) method is used to estimate the parameters of the Gompertz extension distribution based on a complete sample. We have developed a procedure to obtain Bayes estimates of the parameters of the Gompertz extension distribution using Markov Chain Monte Carlo (MCMC) simulation method in Open BUGS, established software for Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. We have obtained the Bayes estimates of the parameters, hazard and reliability functions, and their probability intervals are also presented. We have applied the predictive check method to discuss the issue of model compatibility. A real data set is considered for illustration under uniform and gamma priors. 

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Published

2020-07-01

How to Cite

Chaudhary, A. K., & Kumar, V. (2020). A Bayesian Estimation and Predictionof Gompertz Extension Distribution Using the MCMC Method. Nepal Journal of Science and Technology, 19(1), 142–160. https://doi.org/10.3126/njst.v19i1.29795

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Articles