A Bayesian Analysis of Perks Distribution via Markov Chain Monte Carlo Simulation

Authors

  • Arun Kumar Chaudhary Nepal Commerce Campus, Tribhuvan University, Kathmandu
  • Vijay Kumar Department of Mathematics and Statistics DDU Gorakhpur University, Gorakhpur,

DOI:

https://doi.org/10.3126/njst.v14i1.8936

Keywords:

Bayesian estimation, Markov chain Monte Carlo, maximum likelihood estimation, model validation, Perks distribution, predictive simulation, openBUGS

Abstract

In this paper the Markov chain Monte Carlo (MCMC) method is used to estimate the parameters of Perks distribution based on a complete sample. The procedures are developed to perform full Bayesian analysis of the Perks distributions using MCMC simulation method in OpenBUGS. We obtained the Bayes estimates of the parameters, hazard and reliability functions, and their probability intervals are also presented. We also discussed the issue of model compatibility for the given data set. A real data set is considered for illustration under gamma sets of priors.

Nepal Journal of Science and Technology Vol. 14, No. 1 (2013) 153-166

DOI: http://dx.doi.org/10.3126/njst.v14i1.8936

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Published

2013-10-14

How to Cite

Chaudhary, A. K., & Kumar, V. (2013). A Bayesian Analysis of Perks Distribution via Markov Chain Monte Carlo Simulation. Nepal Journal of Science and Technology, 14(1), 153–166. https://doi.org/10.3126/njst.v14i1.8936

Issue

Vol. 14 No. 1 (2013)

Section

Articles

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