BIASED CROSS-VALIDATION IN A KERNEL REGRESSION ESTIMATION

This article is concerned with the problem of choosing a bandwidth for nonparametric regression. We consider a method based on an biased estimate of mean average squared error. It is seen that the bandwidth chosen by biased cross-validation method, is asymptotically optimal and has small sample variability. In a simulation study, we show that this bandwidth is closer to optimum bandwidth than other bandwidths when the underlying regression function is sufficiently smooth.
Publisher
Japanese Society of Computational Statistics
Issue Date
1995-12
Language
ENG
Citation

JOURNAL OF THE JAPANESE SOCIETY OF COMPUTATIONAL STATISTICS, v.8, no.1, pp.57 - 68

ISSN
0915-2350
URI
http://hdl.handle.net/10203/73656
Appears in Collection
MT-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
  • Hit : 144
  • Download : 0
  • Cited 0 times in thomson ci

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0