BAYESIAN ESTIMATION OF COMPONENT RELIABILITY USING PROGRESSIVELY CENSORED MASKED SYSTEM LIFETIME DATA FROM RAYLEIGH DISTRIBUTION

  • Jitendra Kumar Department of Statistics and DST-CIMS, Banaras Hindu University, Varanasi-221005, INDIA
  • M.S. Panwar Department of Statistics and DST-CIMS, Banaras Hindu University, Varanasi-221005, INDIA
  • Sanjeev K. Tomer Department of Statistics and DST-CIMS, Banaras Hindu University, Varanasi-221005, INDIA
Keywords: Bayesian Estimation, Competing Risk, Gibbs Sampler, Masked Data, Maximum Likelihood Estimation, Rayleigh Distribution

Abstract

Progressive type-II censoring scheme is a very popular scheme adopted by contributors in the fields of reliability and life-testing. In this paper, we consider a problem when this scheme is applied to a life-testing experiment in which each unit under test is a series system and the investigator is interested in obtaining reliability estimates of individual components. Assuming the components lifetimes to be Rayleigh distribution, we present maximum likelihood and Bayesian approaches to estimate the reliability measures of individual components using masked system lifetime data. The Bayes estimates are evaluated using Lindley’s approximation and Gibbs Sampler. The results are illustrated with the help of simulation study.

 

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Published
2014-12-19
Section
Articles