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arXiv · 2006.07673

Kernel Selection in Nonparametric Regression

Hélène Halconruy·Nicolas Marie
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Abstract

In the regression model $Y = b(X) +\sigma(X)\varepsilon$, where $X$ has a density $f$, this paper deals with an oracle inequality for an estimator of $bf$, involving a kernel in the sense of Lerasle et al. (2016), selected via the PCO method. In addition to the bandwidth selection for kernel-based estimators already studied in Lacour, Massart and Rivoirard (2017) and Comte and Marie (2020), the dimension selection for anisotropic projection estimators of $f$ and $bf$ is covered.

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Hélène Halconruy, Nicolas Marie. 2020-06-13. Kernel Selection in Nonparametric Regression. https://doi.org/10.3103/s1066530720010044

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