Kernel Selection in Nonparametric Regression - Archive ouverte HAL
Article Dans Une Revue Mathematical Methods of Statistics Année : 2020

Kernel Selection in Nonparametric Regression

Résumé

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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Dates et versions

hal-02867190 , version 1 (13-06-2020)
hal-02867190 , version 2 (20-03-2021)

Identifiants

  • HAL Id : hal-02867190 , version 2

Citer

Hélène Halconruy, Nicolas Marie. Kernel Selection in Nonparametric Regression. Mathematical Methods of Statistics, 2020, 29 (1), pp.31-55. ⟨hal-02867190v2⟩
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