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 pra...

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Bibliographic Details
Published inMetron (Rome) Vol. 71; no. 2; pp. 105 - 122
Main Authors Gigliarano, Chiara, Muliere, Pietro
Format Journal Article
LanguageEnglish
Published Milan Springer Milan 01.09.2013
Subjects
Mathematics and Statistics
Original Paper
Statistical Theory and Methods
Statistics
Bayesian nonparametrics
62G07
62P20
62N02
Right censored data
Lorenz curve
Gini coefficient
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Summary: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.
ISSN:0026-1424
2281-695X
DOI:10.1007/s40300-013-0009-9