Bayesian Analysis of Two Parameter Complementary Exponential Power Distribution

Authors

  • A. K. Chaudhary Nepal Commerce Campus

DOI:

https://doi.org/10.3126/nccj.v3i1.20244

Keywords:

Bayesian estimation, Complementary exponential power 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 CEP distribution based on a complete sample. A procedure is developed to obtain Bayes estimates of the parameters of the CEP distribution using Markov Chain Monte Carlo (MCMC) simulation method in OpenBUGS, established software for Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. The MCMC methods have been shown to be easier to implement computationally, the estimates always exist and are statistically consistent, and their probability intervals are convenient to construct. The R functions are developed to study the statistical properties, model validation and comparison tools of the distribution and the output analysis of MCMC samples generated from OpenBUGS. A real data set is considered for illustration under uniform and gamma sets of priors.

 NCC Journal

 Vol. 3, No. 1, 2018,   Page: 1-23

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Author Biography

A. K. Chaudhary, Nepal Commerce Campus

Associate Professor

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Published

2018-06-14

How to Cite

Chaudhary, A. K. (2018). Bayesian Analysis of Two Parameter Complementary Exponential Power Distribution. NCC Journal, 3(1), 1–23. https://doi.org/10.3126/nccj.v3i1.20244

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Articles