Open AccessBayesian Estimation Using Progressively Censored Masked Data Under Asymmetric Loss Function
Author(s) -
Sanjeev K. Tomer,
Jitendra Kumar
Publication year - 2015
Publication title -
journal of institute of science and technology
Language(s) - English
Resource type - Journals
eISSN - 2467-9240
pISSN - 2467-9062
DOI - 10.3126/jist.v20i1.13909
Subject(s) - bayes' theorem , bayesian probability , reliability (semiconductor) , approximate bayesian computation , component (thermodynamics) , computer science , statistics , computation , series (stratigraphy) , function (biology) , exponential distribution , bayes estimator , mathematics , econometrics , algorithm , artificial intelligence , physics , paleontology , power (physics) , quantum mechanics , evolutionary biology , inference , biology , thermodynamics
Competing risk modeling is very useful for the assessment of component characteristics in reliability studies. In this paper, we consider the competing risk modeling of progressively censored data when units under lifetest are series system of two components. Assuming the lifetime distributions of components to be exponentially distributed, we obtain Bayes estimate of parameters and components relative risks under asymmetric loss functions. Bayesian computation is done using Lindley’s approximation. A simulation study is presented for numerical illustrations.Journal of Institute of Science and Technology, 2015, 20(1): 40-50