In this paper we consider the Bayesian estimators for the unknown parameters of Gumbel type-II distribution. The Bayesian estimators cannot be obtained in closed forms. Approximate Bayesian estimators are computed using the idea of Lindley’s approximation under different loss functions. The approximate Bayes estimates obtained under the assumption of non-informative priors are compared with the...

This paper deals with Bayesian inference and prediction problems of the Burr type XII distribution based on progressive first failure censored data. We consider the Bayesian inference under a squared error loss function. We propose to apply Gibbs sampling procedure to draw Markov Chain Monte Carlo (MCMC) samples, and they have in turn, been used to compute the Bayes estimates with the help of i...

We consider the chain ladder reserving method in a Bayesian set up, which allows for combining individual claims development data with portfolio information as for instance development patterns from industry-wide data. We derive the Bayes estimators and the credibility estimators within this Bayesian framework. We show that the credibility estimators are exact Bayesian in the case of the expone...

We derive some shrinkage test-estimators and the Bayes estimators for the shape parameter of a Pareto distribution under the general entropy loss (GEL) function. The properties have been studied in terms of relative efficiency. The choices of shrinkage factor are also suggested.

We derive minimax generalized Bayes estimators of regression coefficients in the general linear model with spherically symmetric errors under invariant quadratic loss for the case of unknown scale. The class of estimators generalizes the class considered in Maruyama and Strawderman (2005) to include non-monotone shrinkage functions. AMS subject classification: Primary 62C20, secondary 62J07

Scale parameter of Log logistic distribution has been studied using Bayesian approach. Posterior distribution has derived by using non informative prior. Posterior distribution is not in close form so we have work with quadrature numerical integration. Various loss functions has been utilized to derive the Bayes estimators and their corresponding risks. Simulation study has been performed to co...

For Modified-Weibull distribution we have obtained the Bayes Estimators of scale and shape parameters using Lindley's approximation (L-approximation) under various loss functions. The proposed estimators have been compared with the corresponding MLE for their risks based on corresponding simulated samples. Key-Words: Bayesian estimation, Lindley's approximation, Maximum likelihood estimates, Mo...

Parametric empirical Bayes PEB may perform poorly when the assumed prior distribution is seriously invalid Nonparametric empirical Bayes NEB is more robust since it imposes no restric tion on the prior But compared with the PEB the NEB may be ine cient for small to medium samples due to the large variation and under dispersion of the NPMLE of the prior Using Monte Carlo simulations we compare t...