Inequality measures are often used to summarise information about empirical income distributions. However , the resulting picture of the distribution and of the changes in the distribution can be severely distorted if the data are contaminated. The nature of this distortion will in general...

Lorenz curves and second-order dominance criteria are known to be sensitive to data contamination in the right tail of the distribution. We propose two ways of dealing with the problem: (1) Estimate Lorenz curves using parametric models for income distributions, and (2) Combine empirical...

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...

Much of the trading activity in Equity markets is directed to brokerage houses. In exchange they provide so-called “soft dollars” which basically are amounts spent in “research” for identifying profitable trading opportunities. Soft dollars represent about USD 1 out of every USD 10 paid...

This paper proposes a robust estimator for a general class of linear latent variable models (GLLVM) (Moustaki and Knott 2000, Bartholomew and Knott 1999). It is based on a weighted score function that is simple to implement numerically and is made consistent using the basic idea of indirect...

Poverty analysis often results in the computation of poverty indexes based on so-called poverty lines which can be region specific poverty lines. The poverty lines are made of two components, namely the amount of income to satisfy the food and the non food needs. For both components, one needs...

Generalized linear latent variable models (GLLVMs), as defined by Bartholomew and Knott, enable modelling of relationships between manifest and latent variables. They extend structural equation modelling techniques, which are powerful tools in the social sciences. However, because of the...

In this paper we investigate the robustness properties of the class of minimum power divergence estimators for grouped data. This class contains the classical maximum likelihood estimators for grouped data. We find that the bias of these estimators due to deviations from the assumed underlying...