Most recently proposed bandwidth selectors in kernel density estimation have been developed with intent to reduce the large sampling variability of Least Squares Cross-Validation. Their asymptotic superiority has been shown in many papers. Some of those selectors have even the fastest n-1/ 2...

Increasing dispersion in regression analysis means that with positive changes of the explanatory variable the residual variance increases. Motivated by theoretical questions in stability of demand systems we consider the question of increasing dispersion in a nonparameteric way. It amounts to...

This paper considers the semiparametric stochastic frontier model with panel data which arises in the problem of measuring technical inefficiency in production processes. We assume a parametric form for the frontier function, which is linear in production inputs. The density of the individual...

This paper generalizes the results of Hausman and Taylor (1981), Schmidt and Sickles (1984), Cornwell, Schmidt and Sickles (1990) and Park and Simar (1992) to the efficient IV estimation of panel models in which the random effects are correlated with a subset of the regressors. The model in...

This article proposes a new data-driven method for selecting the smoothing parameter involved in the construction of kernel-based adaptive location estimators. The method consists of minimizing a cross-validatory criterion with respect to the bandwidth occurring in the kemd type estimators of...

In this paper robustness properties are studied for kernel density estimators. A plug-in and least squares cross-validation bandwidth selector are considered. In an asymptotic analysis and in a simulation study it is shown that the robustness of kernel density estimates depends strongly on the...