Bandwidth matrix selectors for multivariate kernel regression

KOLÁČEK, Jan and Ivanka HOROVÁ. Bandwidth matrix selectors for multivariate kernel regression. In SMTDA Book of abstracts 2014. 2014.

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

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Other information

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

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Abstract

In the original language

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.