An empirical study of indirect cross–validation

Olga Savchuk, Jeffrey Hart, Simon Sheather

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations

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 that 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.

Original languageEnglish
Title of host publicationNonparametric Statistics and Mixture Models
Subtitle of host publicationA Festschrift in Honor of Thomas P Hettmansperger
Pages288-308
Number of pages21
ISBN (Electronic)9789814340564
DOIs
StatePublished - Jan 1 2011

Keywords

  • Bandwidth selection
  • Cross-validation
  • Integrated squared error
  • Kernel density estimation
  • Mean integrated squared error

ASJC Scopus subject areas

  • Mathematics (all)

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