Selection of bandwidth for kernel regression

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F16%3A00087701" target="_blank" >RIV/00216224:14310/16:00087701 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1080/03610926.2013.864770" target="_blank" >http://dx.doi.org/10.1080/03610926.2013.864770</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/03610926.2013.864770" target="_blank" >10.1080/03610926.2013.864770</a>

Result language
angličtina
Original language name
Selection of bandwidth for kernel regression
Original language description
The most important factor in kernel regression is a choice of a bandwidth. Considerable attention has been paid to extension the idea of an iterative method known for a kernel density estimate to kernel regression. Data-driven selectors of the bandwidth for kernel regression are considered. The proposed method is based on an optimally balanced relation between the integrated variance and the integrated square bias. This approach leads to an iterative quadratically convergent process. The analysis of statistical properties shows the rationale of the proposed method. In order to see statistical properties of this method the consistency is determined. The utility of the method is illustrated through a simulation study and real data applications.
Czech name
—
Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BA - General mathematics
OECD FORD branch
—

Project
<a href="/en/project/GA15-06991S" target="_blank" >GA15-06991S: Functional data analysis and related topics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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