Bayesian inference for the parameters of Weibull distribution under progressive Type-I interval censored data with beta-binomial removals

ABSTRACT

This article considers the problem of estimating the parameters of Weibull distribution under progressive Type-I interval censoring scheme with beta-binomial removals. Classical as well as the Bayesian procedures for the estimation of unknown model parameters have been developed. The Bayes estimators are obtained under SELF and GELF using MCMC technique. The performance of the estimators, has been discussed in terms of their MSEs. Further, expression for the expected number of total failures has been obtained. A real dataset of the survival times for patients with plasma cell myeloma is used to illustrate the suitability of the proposed methodology.

KEYWORDS:

• Beta Binomial Removals
• Clinical Trials
• Expected Total Number of Failures
• Progressive Type-I Interval Censoring
• Weibull Distribution

MATHEMATICS SUBJECT CLASSIFICATION:

• 62F10
• 62F15
• 62N01
• 62N02
• 62N05

Acknowledgments

The authors thank the reviewer for constructive and pertinent comments, and are also thankful to the referees for their valuable suggestions that improved the original version of the manuscript. First author would like to express gratitude to Council of Scientific and Industrial Research (CSIR), New Delhi, India, for providing financial assistance to this work.