Estimation of $$\mathbf{R} = \mathbf{P}\mathbf{[Y<X<Z]}$$ under Progressive Type-II Censored Data from Weibull Distribution
Neha Choudhary, Abhishek Tyagi, Bhupendra Singh
Abstract
The study deals with the classical and Bayesian estimation of the stress-strength reliability of the form $$R=\textrm{P}[Y<X<Z]$$ under progressive Type-II censoring scheme, when $$X$$ , $$Y$$ , and $$Z$$ are independent Weibull distributed random variables. In classical setup, we obtain maximum likelihood and uniformly minimum variance unbiased estimator of $$R$$ . Further, assuming independent gamma priors for the unknown parameters, Bayes estimate of $$R$$ under squared error loss function is derived. The biases and mean square errors are used to compare the performances of the proposed estimators. One real data analysis is performed for illustrative purposes.
Topics & Concepts
MathematicsWeibull distributionEstimatorCensoring (clinical trials)StatisticsBayes estimatorMean squared errorRandom variablePrior probabilityCombinatoricsMaximum likelihoodMinimum-variance unbiased estimatorType (biology)Applied mathematicsBayesian probabilityEcologyBiologyStatistical Distribution Estimation and ApplicationsProbabilistic and Robust Engineering DesignStatistical Methods and Bayesian Inference