Empirical study of indirect cross-validation

TL;DR

This paper empirically evaluates indirect cross-validation (ICV) for kernel density estimation, demonstrating its superior stability and performance over least squares cross-validation (LSCV) through simulations and real data examples.

Contribution

It introduces ICV as a practical bandwidth selection method and provides empirical evidence of its advantages over LSCV.

Findings

ICV generally outperforms LSCV in finite samples

ICV offers increased stability compared to LSCV

Real data examples confirm the benefits of ICV

Abstract

In this paper we provide insight into the empirical properties of indirect cross-validation (ICV), a new method of bandwidth selection for kernel density estimators. First, we describe the method and report on the theoretical results used to develop a practical-purpose model for certain ICV parameters. Next, we provide a detailed description of a numerical study which shows that the ICV method usually outperforms least squares cross-validation (LSCV) in finite samples. One of the major advantages of ICV is its increased stability compared to LSCV. Two real data examples show the benefit of using both ICV and a local version of ICV.