Empirical study of indirect cross-validation

Details Empirical study of indirect cross-validation Empirical study of indirect cross-validation

2008-11-29

arxiv.org/abs/0812.0052v1

Statistics

Methodology

22 pages, 21 figures

Scientific paper

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.

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