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Scientific article
English

Robust Methods for Personal-Income Distribution Models

Published inCanadian journal of statistics, vol. 22, no. 2, p. 247-258
Publication date1994
Abstract

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.

Keywords
  • Inequality meaasures
  • Parametric models
  • Influence function
  • M-estimators
  • Incomplete data
  • EM-algorithm
Citation (ISO format)
VICTORIA-FESER, Maria-Pia, RONCHETTI, Elvezio. Robust Methods for Personal-Income Distribution Models. In: Canadian journal of statistics, 1994, vol. 22, n° 2, p. 247–258.
Identifiers
  • PID : unige:22957
ISSN of the journal0319-5724
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