Estimating the Lorenz curve and Gini index with right censored data: a Polya tree approach

In this paper we estimate income distributions, Lorenz curves and the related Gini index using a Bayesian nonparametric approach based on Polya tree priors. In particular, we propose an alternative approach for dealing with contaminated observations and extreme income values: avoiding the common practise that removes these critical data, we instead treat them as censored observations and apply a Polya tree model for incomplete data. The proposed method is illustrated through an empirical application based on the European Survey on Income Living Conditions data. Copyright Sapienza Università di Roma 2013