infonet-economy

In this paper robustness properties of the maximum likelihood estimator (MLE) and several robust estimators for the logistic regression model when the responses are binary are analysed analytically by means of the Influence Function (IF) and empirically by means of simulations. It is found that the MLE and the classical Rao's score test can be misleading in the presence of model...

Distributional dominance criteria are commonly applied to draw welfare inferences about comparisons, but conclusions drawn from empirical implementations of dominance criteria may be influenced by data contamination. We show the conditions under which this may occur and propose empirical methods to work round the proble using both non-parametric and parametric approaches.

We show how a collection of results in the literature on the empirical estimation of welfare indicators from sample data can be unified. We also demonstrate how some of these ideas can be extended to empirically important cases where the data have been trimmed or censored.

Lorenz curves and associated tools for ranking income distributions are commonly estimated on the assumption that full, unbiased samples are available. However it is common to ¯nd income and wealth distributions that are routinely censored or trimmed. We derive the sampling distribution for a key family of statistics in the case where data have been modified in this fashion.

This paper presents a robust estimation of two income distribution models using Spanish data for the period 1990-91 under three different concepts of income. The effect on the estimates of the Theil index due to the choice of the definition of income and of the estimation method is also analysed.

In this paper we propose a robust version of Cox-type test statistics for the choice between two non-nested hypotheses. We first show that the influence of small amounts of contamination in the data on the test decision can be very large. Secondly we build a robust test statistic by using the results on robust parametric tests available in the literature and show that the level of...

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 leastsquares estimators. Unfortunately, the classical methods are very sensitive to model deviations such as gross errors in the data, grouping effects...