Strong convergence of robust equivariant nonparametric functional regression estimators

被引：15
|
作者：
Boente, Graciela [1 ,2 ]
Vahnovan, Alejandra [3 ,4 ]
机构：
[1] Univ Buenos Aires, Fac Ciencias Exactas & Nat, Dept Matemat, RA-1428 Buenos Aires, DF, Argentina
[2] Consejo Nacl Invest Cient & Tecn, IMAS, RA-1428 Buenos Aires, DF, Argentina
[3] Univ Nacl La Plata, Fac Ciencias Exactas, Dept Matemat, RA-1900 La Plata, Buenos Aires, Argentina
[4] Consejo Nacl Invest Cient & Tecn, La Plata, Buenos Aires, Argentina
来源：
STATISTICS & PROBABILITY LETTERS | 2015年 / 100卷
关键词：
Functional data; Kernel weights; M-location functionals; Robust estimation; SMALL BALL PROBABILITIES; GAUSSIAN-PROCESSES; PREDICTION; RATES;
D O I：
10.1016/j.spl.2015.01.028
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：
Robust nonparametric equivariant M-estimators for the regression function have been extensively studied when the covariates are in R-k. In this paper, we derive strong uniform convergence rates for kernel-based robust equivariant M-regression estimator when the covariates are functional. (C) 2015 Elsevier B.V. All rights reserved.
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收藏
页码：1 / 11
页数：11
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