Kernel Selection in Nonparametric Regression

Abstract

In the regression model $Y=b(X)+\sigma (X)\epsilon$, 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.

23 pages

References in corpus (2)

• Bandwidth Selection for the Wolverton-Wagner Estimator
• On a Nadaraya-Watson Estimator with Two Bandwidths

Cited by in corpus (1)

• Nadaraya-Watson Estimator for I.I.D. Paths of Diffusion Processes