Averaging of density kernel estimators

被引：0
|
作者：
Chernova, O. [1 ]
Lavancier, F. [2 ]
Rochet, P. [2 ]
机构：
[1] Taras Shevchenko Natl Univ Kyiv, 64 Volodymyrska, UA-01601 Kiev, Ukraine
[2] Univ Nantes, Lab Math Jean Leray, 2 Rue Houssiniere, F-44322 Nantes, France
来源：
STATISTICS & PROBABILITY LETTERS | 2020年 / 158卷
关键词：
Aggregation; Bandwidth selection; Non-parametric estimation; BANDWIDTH SELECTION; CROSS-VALIDATION; AGGREGATION;
D O I：
10.1016/j.spl.2019.108645
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 study the theoretical properties of a linear combination of density kernel estimators obtained from different data-driven bandwidths. The average estimator is proved to be asymptotically as efficient as the oracle, with a control on the error term. The performances are tested numerically, with results that compare favorably to other existing procedures. (C) 2019 Elsevier B.V. All rights reserved.
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页数：5
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