Premium
Robust methods for personal‐income distribution models
Author(s) -
VictoriaFeser MariaPia,
Ronchetti Elvezio
Publication year - 1994
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315587
Subject(s) - estimator , econometrics , personal income , estimation , mathematics , distribution (mathematics) , least squares function approximation , maximum likelihood , statistics , economics , mathematical analysis , management , economic growth
Statistical problems in modelling personal‐income distributions include estimation procedures, testing, and model choice. Typically, the parameters of a given model are estimated by classical procedures such as maximum‐likelihood and least‐squares estimators. Unfortunately, the classical methods are very sensitive to model deviations such as gross errors in the data, grouping effects, or model misspecifications. These deviations can ruin the values of the estimators and inequality measures and can produce false information about the distribution of the personal income in a country. In this paper we discuss the use of robust techniques for the estimation of income distributions. These methods behave like the classical procedures at the model but are less influenced by model deviations and can be applied to general estimation problems.