Towards Semiparametric Bandwidth Selectors for Kernel Density Estimators

TL;DR

This paper explores semiparametric bandwidth selection methods for kernel density estimators using Hermite expansions, aiming to improve upon existing nonparametric and parametric approaches with minimal additional complexity.

Contribution

It introduces a new class of semiparametric bandwidth selectors based on Hermite expansions, extending the normal reference rule with low-dimensional corrections.

Findings

Proposes Hermite-based semiparametric bandwidth estimators

Provides initial theoretical framework and ideas

Highlights need for further analysis and simulations

Abstract

There is an intense and partly recent literature focussing on the problem 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 selectors, via Hermite type expansions aroundthe normal distribution. 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 discusses 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, numerical work, and some simulation-based exploration.