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BAYESIAN ESTIMATION OF THE SHAPE PARAMETER OF THE GENERALISED EXPONENTIAL DISTRIBUTION UNDER DIFFERENT LOSS FUNCTIONS

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Author(s): SANKU DEY

Journal: Pakistan Journal of Statistics and Operation Research
ISSN 1816-2711

Volume: 6;
Issue: 2;
Date: 2010;
Original page

Keywords: Bayes estimator | prior distribution | loss functions | root mean square error(rmse) | efficiency.

ABSTRACT
The generalized exponential (GE) distribution proposed by Gupta and Kundu (1999) is an important lifetime distribution in survival analysis. In this article, we propose to obtain Bayes estimators and its associated risk based on a class of  non-informative prior under the assumption of three loss functions, namely, quadratic loss function (QLF), squared log-error loss function (SLELF) and general entropy loss function (GELF). The motivation is to explore the most appropriate loss function among these three loss functions. The performances of the estimators are, therefore, compared on the basis of their risks obtained under QLF, SLELF and GELF separately. The relative efficiency of the estimators is also obtained. Finally, Monte Carlo simulations are performed to compare the performances of the Bayes estimates under different situations.
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