Linear errors-in-covariables models are considered, assuming the availability of independent validation data on the covariables in addition to primary data on the response variable and surrogate covariables. We first develop an estimated empirical log-likelihood with the help of validation data...

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...