Generalised Exponential Kernels for Nonparametric Density Estimation

TL;DR

This paper presents a new kernel density estimator based on the generalised exponential distribution, offering a mathematically simpler yet flexible alternative for positive data, with proven optimality and competitive performance.

Contribution

The paper introduces a novel GE kernel density estimator that avoids special functions, derives its asymptotic properties, and demonstrates its optimality and effectiveness through experiments.

Findings

GE KDE has similar flexibility to gamma KDE

The second GE KDE achieves optimal mean integrated squared error

Numerical experiments show competitive performance

Abstract

This paper introduces a novel kernel density estimator (KDE) based on the generalised exponential (GE) distribution, designed specifically for positive continuous data. The proposed GE KDE offers a mathematically tractable form that avoids the use of special functions, for instance, distinguishing it from the widely used gamma KDE, which relies on the gamma function. Despite its simpler form, the GE KDE maintains similar flexibility and shape characteristics, aligning with distributions such as the gamma, which are known for their effectiveness in modelling positive data. We derive the asymptotic bias and variance of the proposed kernel density estimator, and formally demonstrate the order of magnitude of the remaining terms in these expressions. We also propose a second GE KDE, for which we are able to show that it achieves the optimal mean integrated squared error, something that is difficult to establish for the former. Through numerical experiments involving simulated and real data sets, we show that GE KDEs can be an important alternative and competitive to existing KDEs.