A Study of Perks-II Distribution via Bayesian Paradigm

Authors

  • A. K. Chaudhary Nepal Commerce Campus

DOI:

https://doi.org/10.3126/pravaha.v24i1.20221

Keywords:

Bayesian estimation, Maximum likelihood estimation, Markov chain Monte Carlo, Model validation, OpenBUGS, Perks-II distribution

Abstract

In this paper, the Markov chain Monte Carlo (MCMC) method is used to estimate the parameters of Perks-II distribution based on a complete sample. The procedures are developed to perform full Bayesian analysis of the Perks-II distributions using Markov Chain Monte Carlo (MCMC) simulation method in OpenBUGS, 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 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.

Pravaha

Vol. 24, No. 1, 2018,page: 1-17

 

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Author Biography

A. K. Chaudhary, Nepal Commerce Campus

Associate Professor

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Published

2018-06-12

How to Cite

Chaudhary, A. K. (2018). A Study of Perks-II Distribution via Bayesian Paradigm. Pravaha, 24(1), 1–17. https://doi.org/10.3126/pravaha.v24i1.20221

Issue

Vol. 24 No. 1 (2018)

Section

Articles

License

© Nepal Commerce Campus, TU

Authors are required to transfer their copyright to the Nepal Commerce Campus, TU.