Optimal Kernel Selection for Density Estimation

被引：6

作者：

Lerasle, Matthieu ^{[1
]}

Magalhaes, Nelo Molter ^{[2
]}

Reynaud-Bouret, Patricia ^{[1
]}

机构：

[1] Univ Cote Azur, CNRS, LJAD, UMR 7351, F-06100 Nice, France

[2] Univ Paris 11, INRIA, Dept Math Orsay, Select Project, F-91405 Orsay, France

来源：

HIGH DIMENSIONAL PROBABILITY VII: THE CARGESE VOLUME | 2016年 / 71卷

关键词：

Density estimation; Kernel estimators; Minimal penalty; Optimal penalty; Oracle inequalities; MOMENT INEQUALITIES; PENALTIES; UNIFORM;

D O I：

10.1007/978-3-319-40519-3_19

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We provide new general kernel selection rules thanks to penalized least-squares criteria. We derive optimal oracle inequalities using adequate concentration tools. We also investigate the problem of minimal penalty as described in Birge and Massart (2007, Probab. Theory Relat. Fields, 138(1-2):33-73).

引用

页码：425 / 460

页数：36

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