Classical parametric estimation methods applied to nonlinear regression and limited-dependent-variable models are very sensitive to misspecification and data errors. On the other hand, semiparametric and nonparametric methods, which are not restricted by parametric assumptions, require more data...

In this work, we introduce a smoothed influence function that constitute a theoretical tool for studying the outliers robustness properties of a large class of nonparametric estimators. With this tool, we first show the nonrobustness of the Nadaraya-Watson estimator of regression. Then we show...

Deviations from the center within a robust neighborhood may naturally be considered an infinite dimensional nuisance parameter. Thus, in principle, the semiparametric method may be tried, which is to compute the scores function for the main parameter minus its orthogonal projection on the closed...

We analyze the impact of an individual's tendency to worry on willingness to pay (WTP) for a protective measure. We report on the results of a controlled experiment with real objects at stake. Worry was measured with the Worry Domains Questionnaire, an instrument determining an individual's...

The paper concerns the fixed-width confidence intervals for location based on M- estimators in the location model. A robust three-stage procedure is proposed and its asymptotic properties are studied. The performance of the procedure depends on some tuning parameters. Their effect on the...

In empirical applications of structural equation modeling researchers often assume that the sample under investigation is homogenous unless observed characteristics allow for a division of the sample into mutual exclusive homogenous subgroups. If such information is not available, unobserved...

Healthcare expenditure has increased substantially in all western industrialized countries in the last decades. The necessity to contain the increase in health care expenditure has motivated the Analysis of its determinants to explain differences across countries and health systems. However,...

We consider the partially linear model relating a response Y to predictors (X,T) with mean function XT ß + g (T) when the X's are measured with additive error. The semiparametric likelihood estimate of Severini and Staniswalis (1994) leads to biased estimates of both the parameter ß and the...