KOLÁČEK, Jan and Ivanka HOROVÁ. Bandwidth matrix selectors for multivariate kernel regression. In 3rd Stochastic Modeling Techniques and Data Analysis International Conference. 2014.
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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.
Other information
Original language English
Type of outcome Presentations at conferences
Field of Study 10101 Pure mathematics
Country of publisher Portugal
Confidentiality degree is not subject to a state or trade secret
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: 17/7/2014 15:58.
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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