Kernel bandwidth selection for a first order nonparametric streamflow simulation model (Q1287036)

This study documents some of the tests that were conduced to evaluate the performance of bandwidth estimation methods for kernel density estimation. We examine issues related to selection of optimal smoothing parameters for kernel density estimation with small samples (200 or fewer data points). Both reference to a Gaussian density and data based specifications are applied to estimate bandwidths for samples from bivariate normal mixture densities. The three data based methods studied are maximum likelihood cross-validation, least-square cross-validation, and biased cross-validation.