Author(s):
1. Younshik Chung:
Department of Statistics, Pusan National University,Busan,Korea
2. Dipak K. Dey:
Department of Statistics, University of Connecticut,USA
3. Myoungjin Jung:
Department of Statistics, Pusan National University,Busan,Korea
Abstract:
In recent years, there are many efforts to develop new statistical distribution with more flexibility which can be fitted well to complex data. In this paper we develop a compounded distribution of two-parameter inverse Weibull with geometric random variables, so-called the exponentiated inverse Weibull geometric (EIWG) distribution. We study the properties of the EIWG distribution such as moments, moment generating function, mean deviations, Bonferroni and Lorenz curves, entropies and order statistics, and obtain the maximum likelihood estimations of the parameter of the distribution using EM algorithm and Bayesian estimations using Gibbs sampler. Finally, the EIWG distribution is applied to model a simulated and real failure data, with appropriate model comparison with the competing models.
Page(s):
161-178
DOI:
DOI not available
Published:
Journal: Pakistan Journal of Statistics, Volume: 33, Issue: 3, Year: 2017
Keywords:
Bayesian estimation
,
MetropolisHastings algorithm
,
Exponentiated inverse Weibull geometric EIWG distribution
,
Inverse Weibull distribution
,
Gibbs sampler
,
Maximum likelihood estimation MLE
References:
References are not available for this document.
Citations
Citations are not available for this document.