Bandwidth matrix selectors for multivariate kernel regression

KOLÁČEK, Jan and Ivanka HOROVÁ. Bandwidth matrix selectors for multivariate kernel regression. In 3rd Stochastic Modeling Techniques and Data Analysis International Conference. 2014.

Basic information

Original name

Bandwidth matrix selectors for multivariate kernel regression

Authors

KOLÁČEK, Jan and Ivanka HOROVÁ

Edition

3rd Stochastic Modeling Techniques and Data Analysis International Conference, 2014

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Other information

Language

English

Type of outcome

Prezentace na konferencích

Field of Study

10101 Pure mathematics

Country of publisher

Portugal

Confidentiality degree

není předmětem státního či obchodního tajemství

Organization unit

Faculty of Science

Keywords in English

multivariate kernel regression; constrained bandwidth matrix; MSE

Tags

International impact, Reviewed

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Abstract

V originále

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.