Bandwidth Selection Problem in Nonparametric Functional Regression

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F17%3A00094996" target="_blank" >RIV/00216224:14310/17:00094996 - isvavai.cz</a>
Result on the web
<a href="https://www.czso.cz/documents/10180/45606531/32019717q3107.pdf/d06c45a7-674c-4c4f-ac7b-dfea3293e915?version=1.0" target="_blank" >https://www.czso.cz/documents/10180/45606531/32019717q3107.pdf/d06c45a7-674c-4c4f-ac7b-dfea3293e915?version=1.0</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Bandwidth Selection Problem in Nonparametric Functional Regression
Original language description
The focus of this paper is the nonparametric regression where the predictor is a functional random variable, and the response is a scalar. Functional kernel regression belongs to popular nonparametric methods used for this purpose. The two key problems in functional kernel regression are choosing an optimal smoothing parameter and selecting an appropriate semimetric as a distance measure. The former is the focus of this paper – several data-driven methods for optimal bandwidth selection are described and discussed. The performance of these methods is illustrated in a real data application. A conclusion is drawn that local bandwidth selection methods are more appropriate in the functional setting.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability

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
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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