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

被引：4
作者：
Gigliarano C. [1 ]
Muliere P. [2 ]
机构：
[1] Department of Economics and Social Sciences, Università Politecnica delle Marche, 60121 Ancona, Piazzale Martelli
[2] Department of Decision Sciences, Bocconi University, Milan
来源：
METRON | 2013年 / 71卷 / 2期
关键词：
Bayesian nonparametrics; Gini coefficient; Lorenz curve; Right censored data;
D O I：
10.1007/s40300-013-0009-9
中图分类号：
学科分类号：
摘要：
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. © Sapienza Università di Roma 2013.
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页码：105 / 122
页数：17
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