UNIGE document Report
previous document  unige:6622  next document
add to browser collection

Robust Income Estimation with Missing Data

Publication London, 2000
Collection DARP discussion paper; 57
Description 21 p.
Abstract With income distributions it is common to encounter the problem of missing data. When a parametric model is fitted to the data, the problem can be overcome by specifying the marginal distribution of the observed data. With classical methods of estimation such as the maximum likelihood (ML) an estimator of the parameters can be obtained in a straightforward manner. Unfortunately, it is well known that ML estimators are not robust estimators in the presence of contaminated data. In this paper, we propose a robust alternative to the ML estimator with truncated data, namely one based on Mestimators that we call the EMM estimator. We present an extensive simulation study where the EMM estimator based on optimal B-robust estimators (OBRE) is compared to a more conservative approach based on marginal density (MD) for truncated data, and show that the difference lies in the way the weights associated to each observation are computed. Finally, we also compare the EMM estimator based on the OBRE with the classical ML estimator when the data are contaminated, and show that contrary to the former, the latter can be seriously biased.
Keywords M-estimatorsInfluence functionEM algorithmTruncated data
Full text
Report (312 Kb) - public document Free access
(ISO format)
VICTORIA-FESER, Maria-Pia. Robust Income Estimation with Missing Data. 2000 https://archive-ouverte.unige.ch/unige:6622

517 hits



Deposited on : 2010-05-18

Export document
Format :
Citation style :