Affiliations: Department of Statistics, Panjab University, Chandigarh, India
Corresponding author: Sangeeta Arora, Department of Statistics, Panjab University, Chandigarh-160014, India, Tel.: +91 0172 2541776 (office), +91 9876366604; E-mail: [email protected].
Abstract: Bayes estimators are obtained in case of Pareto distribution for its shape parameter, mean income, Gini index and a Poverty measure for both censored and complete setup. The said estimators are obtained using Jeffreys' non-informative invariant prior and the extension of Jeffreys' prior information. Using simulation techniques, the relative efficiency of proposed estimators with the existing estimators using two-parameter exponential prior is obtained. It turns out that the Bayesian method with Jeffreys' non-informative invariant prior results in smaller expected loss function as compared to existing estimators using two-parameter exponential prior.
Keywords: Gini index, poverty measure, bayes estimator, pareto distribution, non-informative prior, squared error loss function (SELF)