Estimation of P(Y < X) for progressively first-failure-censored generalized inverted exponential distribution

ABSTRACT

In this article, we consider the problem of estimation of the stress–strength parameter δ = P(Y < X) based on progressively first-failure-censored samples, when X and Y both follow two-parameter generalized inverted exponential distribution with different and unknown shape and scale parameters. The maximum likelihood estimator of δ and its asymptotic confidence interval based on observed Fisher information are constructed. Two parametric bootstrap boot-p and boot-t confidence intervals are proposed. We also apply Markov Chain Monte Carlo techniques to carry out Bayes estimation procedures. Bayes estimate under squared error loss function and the HPD credible interval of δ are obtained using informative and non-informative priors. A Monte Carlo simulation study is carried out for comparing the proposed methods of estimation. Finally, the methods developed are illustrated with a couple of real data examples.

KEYWORDS:

• Stress–strength model
• maximum likelihood estimation
• bootstrap confidence interval
• MCMC technique
• Bayes estimation
• HPD credible interval

MATHEMATICAL SUBJECT CLASSIFICATION 2010:

• Primary: 62N01
• 62N05
• Secondary: 62F10
• 62F15

Acknowledgments

The authors express their sincere thanks to anonymous reviewer for his constructive comments and useful suggestions which led to improvement in the quality of this article.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Hare Krishna http://orcid.org/0000-0002-2585-3232

Madhulika Dube http://orcid.org/0000-0003-0973-3043

Renu Garg http://orcid.org/0000-0002-5869-3386