Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/90468
Authors: 
Köhler, Max
Schindler, Anja
Sperlich, Stefan
Year of Publication: 
2011
Series/Report no.: 
Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 95
Abstract: 
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.
Subjects: 
Kernel regression estimation
Bandwidth Selection
Plug-in
Cross Validation
Document Type: 
Working Paper

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