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Exponentiated Gumbel Model for Software Reliability Data Analysis using MCMC System

by Ashwini Kumar Srivastava, Vijay Kumar, Raj Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 20
Year of Publication: 2013
Authors: Ashwini Kumar Srivastava, Vijay Kumar, Raj Kumar
10.5120/10212-5050

Ashwini Kumar Srivastava, Vijay Kumar, Raj Kumar . Exponentiated Gumbel Model for Software Reliability Data Analysis using MCMC System. International Journal of Computer Applications. 62, 20 ( January 2013), 24-32. DOI=10.5120/10212-5050

@article{ 10.5120/10212-5050,
author = { Ashwini Kumar Srivastava, Vijay Kumar, Raj Kumar },
title = { Exponentiated Gumbel Model for Software Reliability Data Analysis using MCMC System },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 20 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 24-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number20/10212-5050/ },
doi = { 10.5120/10212-5050 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:12:24.210135+05:30
%A Ashwini Kumar Srivastava
%A Vijay Kumar
%A Raj Kumar
%T Exponentiated Gumbel Model for Software Reliability Data Analysis using MCMC System
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 20
%P 24-32
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The proposed model has been a sweeping statement of the classical Gumbel model. In this paper, the Markov chain Monte Carlo (MCMC) system has been used to estimate the parameters of Exponentiated Gumbel(EG) model based on a complete sample. A procedure is developed to obtain Bayes estimates of the parameters of the Exponentiated Gumbel model using MCMC simulation method in OpenBUGS, an established software for Bayesian analysis using Markov Chain Monte Carlo (MCMC) system. The MCMC methods have been shown to be easy 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 proposed model and the output analysis of MCMC samples generated from OpenBUGS. The proposed methodology is suitable for empirical modeling. A simulated data set is considered for illustration under uniform and gamma sets of priors.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Exponentiated Gumbel(EG) model Parameter estimation Maximum likelihood estimate (MLE) Bayes estimates Markov Chain Monte Carlo (MCMC) OpenBUGS

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