Some bayes' estimators for laplace distribution under different loss functions

Abstract EN

Events generated by two random processes.

It can also be used to describe breaking strength data, modeling the differences in flood stages, etc.

(Krishnamoorthy, 2006).

(Julia and Vives-Rego, 2008) presented an application of the skew-Laplace distribution to flow cytometry data.

(Abbasi, 2011) has dealt with the aspect of the Bayesian inference of the discrete Laplace distribution ; she made comparison between the Bayes' estimator and the maximum entropy estimator for discrete Laplace distribution.

(Ali, S., 2010) submitted Baysian analysis of simple and mixture of Laplace distribution ; he presented an overall comparison using various types of informative and non informative priors under different types of loss functions.

In this study we present comparison of maximum likelihood estimator and some Bayes' estimators for the scale parameter b, of Laplace distribution.

It is arranged as follows : Maximum likelihood estimator, Bayes' estimators with the extension of Jeffreys prior and new suggested prior called the modified inverse gamma prior are presented under the squared and modified squared error loss functions.

Comparison was made through a Monte Carlo simulation study on the performance of these estimators.

The results are summarized in tables and followed by the conclusions.