A Review and Comparison of Bandwidth Selection Methods for Kernel Regression - Max Köhler; Anja Schindler; Stefan Sperlich (Georg-August-Universität Göttingen, Courant Research Centre - Poverty, Equity and Growth (CRC-PEG), 2011)
Over the last four decades, several methods for selecting the smoothing parameter, generally called the bandwidth, have been introduced in kernel regression. They differ quite a bit, and although there already exist more selection methods than for any other regression smoother we can still see coming up new ones. Given the need of automatic data-driven bandwidth selectors for applied statistics, this review is intended to explain and compare these methods.
Keywords: 330; Kernel regression estimation; Bandwidth Selection; Plug-in; Cross Validation