KOLÁČEK, Jan and Ivanka HOROVÁ. Bandwidth matrix selectors for multivariate kernel regression. In SMTDA Book of abstracts 2014. 2014.
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Basic information
Original name Bandwidth matrix selectors for multivariate kernel regression
Authors KOLÁČEK, Jan (203 Czech Republic, guarantor, belonging to the institution) and Ivanka HOROVÁ (203 Czech Republic, belonging to the institution).
Edition SMTDA Book of abstracts 2014. 2014.
Other information
Original language English
Type of outcome Conference abstract
Field of Study 10103 Statistics and probability
Country of publisher Portugal
Confidentiality degree is not subject to a state or trade secret
RIV identification code RIV/00216224:14310/14:00076917
Organization unit Faculty of Science
Keywords in English multivariate kernel regression; constrained bandwidth matrix; MSE
Tags International impact, Reviewed
Changed by Changed by: doc. Mgr. Jan Koláček, Ph.D., učo 19999. Changed: 16/3/2015 09:27.
Abstract
The most important factor in multivariate kernel regression is a choice of a bandwidth matrix. This choice is particularly important, because of its role in controlling both the amount and the direction of multivariate smoothing. Considerable attention has been paid to constrained parameterization of the bandwidth matrix such as a diagonal matrix. The proposed method is based on an optimally balanced relation between the integrated variance and the integrated squared bias. The utility of the method is illustrated through a simulation study and real data applications.
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