  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.
