Abstract
It is recognized that, for Bayes estimators, the performance depends on the form of the prior distribution and the assumed loss function. This paper resolves the problem of estimation of one parameter decapitated generalized Poisson distribution; using class of improper prior distributions under symmetric and asymmetric loss functions. The statistical performances of the Bayes estimates with respect to different priors and loss functions are compared using mean square error based on simulation study.
Highlights
Discrete distributions are finding their way into Bayesian analysis
The literature dealing with discrete distributions is sparse as far as Bayesian inference is concerned
This loss is a generalization of the Entropy loss, referred to by several authors (see, Dey et al (1987) and Dey and Liu (1992)), where the shape parameter p is equal to 1.The Bayes estimator for the parameter under General
Summary
Discrete distributions are finding their way into Bayesian analysis. The literature dealing with discrete distributions is sparse as far as Bayesian inference is concerned. Considering a random variable X that follows a decapitated generalized Poisson distribution (DGPD) with one parameter when the other parameter is assumed to be known, the probability function of. Hassan et al (2007a) studied Bayes estimator and reliability function of parameter of DGPD by considering a noninformative prior, when the other parameter is assumed to be known. For a bibliography of relevant literature see Jani and Shah (1981), Consul and Famoye (1989), Consul (1989), Johnson, Kotz and Kemp (1992) and Consul and Famoye (2006). This paper sites Bayes estimators of decapitated generalized Poisson distribution (DGPD) for one parameter , when the other parameter is assumed to be known under different loss functions
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