The generalized Normal distribution is obtained from normal distribution by adding a shape parameter to it. This paper is based on the estimation of the shape and scale parameter of generalized Normal distribution by using the maximum likelihood estimation and Bayesian estimation method via Lindley approximation method under Jeffreys prior and informative priors. The objective of this paper is to see which is the suitable prior for the shape and scale parameter of generalized Normal distribution. Simulation study with varying sample sizes, based on MSE, is conducted in R-software for data analysis.

Dagum distribution is a statistical distribution used closely for fitting income and wealth distributions. This distribution has wide application in fields like reliability theory survival analysis, actuarial sciences, and meteorological data. In this article, we obtained Bayes estimators for the shape parameter of Dagum distribution using approximation techniques like normal and T-K approximations. Moreover different informative priors have been considered and a simulation study and three real data sets have been considered to study the efficiency of obtained results.

The present study is concerned with the estimation of Inverse Exponential distribution using various Bayesian approximation techniques like normal approximation, Tierney and Kadane (T-K) Approximation. Different informative and non-informative priors are used to obtain the Baye’s estimate of Inverse Exponential distribution under different approximation techniques. A simulation study has also been conducted for comparison of Baye’s estimates obtained under different approximation using different priors.