Kernel estimation of regression function gradient

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F20%3A00115009" target="_blank" >RIV/00216224:14310/20:00115009 - isvavai.cz</a>
Result on the web
<a href="https://www.tandfonline.com/doi/full/10.1080/03610926.2018.1532518" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/03610926.2018.1532518</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/03610926.2018.1532518" target="_blank" >10.1080/03610926.2018.1532518</a>

Result language
angličtina
Original language name
Kernel estimation of regression function gradient
Original language description
The present paper is focused on kernel estimation of the gradient of a multivariate regression function. Despite the importance of estimating partial derivatives of multivariate regression functions, the progress is rather slow. Our aim is to construct the gradient estimator using the idea of a local linear estimator for the regression function. The quality of this estimator is expressed in terms of the Mean Integrated Square Error. We focus on a crucial problem in kernel gradient estimation the choice of bandwidth matrix. Further, we present some data-driven methods for its choice and develop a new approach based on Newton's iterative process. The performance of presented methods is illustrated using a simulation study and real data example.
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

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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