Bandwidth matrix selectors for multivariate kernel regression
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F14%3A00076917" target="_blank" >RIV/00216224:14310/14:00076917 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Bandwidth matrix selectors for multivariate kernel regression
Original language description
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.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů