Kernel estimation of regression function gradient

Monika Kroupová, Ivana Horová and Jan Koláček

Communications in Statistics - Theory and Methods, 2020, vol. 49, issue 1, 135-151

Abstract: This paper is focused on kernel estimation of the gradient of a multivariate regression function. Despite the importance of this topic, the progress in this area is rather slow. Our aim is to construct a gradient estimator using the idea of local linear estimator for a regression function. The quality of this estimator is expressed in terms of the Mean Integrated Square Error. We focus on a choice of bandwidth matrix. Further, we present some data-driven methods for its choice and develop a new approach. The performance of presented methods is illustrated using a simulation study and real data example.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:49:y:2020:i:1:p:135-151

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DOI: 10.1080/03610926.2018.1532518

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