Towards Semiparametric Bandwith Selectors for Kernel Density Estimators

Abstract

There is an intense and partly recent literature focussing on the problems of selecting the bandwidth parameter for kernel density estimators. Available methods are largely 'very nonparametric', in the sense of not requiring any knowledge about the underlying density, or 'very parametric', like the normality-based reference rule. This report aims at widening the scope towards the inclusion of many semiparametric bandwidth selectorts, via Hermite type expansions around the normal ditstribution. The resulting bandwidths may be seen as carrying out suitable corrections on the normal reference rule, requiring a low number of extra coefficients to be estimated from data The present report introduces and dicusses some basic ideas and develops the necessary initial theory, but modestly chooses to stop short of giving precise recommendations for specific procedures among the many possible constructions. This will require some further analysis and some simulation-based exploration. Future work in this direction is planned with esteemed colleagues I. Gijbels and M. C. Jones.