Robust estimates in generalized partially linear models

被引：45

作者：

Boente, Graciela

He, Xuming

Zhou, Jianhui

机构：

[1] Univ Buenos Aires, Fac Ciencias Exactas & Nat, Dept Matemat, RA-1428 Buenos Aires, DF, Argentina

[2] Univ Buenos Aires, Fac Ciencias Exactas & Nat, Inst Calculo, RA-1428 Buenos Aires, DF, Argentina

[3] Univ Illinois, Dept Stat, Champaign, IL 61820 USA

[4] Univ Virginia, Dept Stat, Charlottesville, VA 22904 USA

[5] Consejo Nacl Invest Cient & Tecn, RA-1033 Buenos Aires, DF, Argentina

来源：

ANNALS OF STATISTICS | 2006年 / 34卷 / 06期

关键词：

kernel weights; partially linear models; rate of convergence; robust estimation; smoothing;

D O I：

10.1214/009053606000000858

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 [统计学]; 070103 [概率论与数理统计]; 0714 [统计学];

摘要：

In this paper, we introduce a family of robust estimates for the parametric and nonparametric components under a generalized partially linear model, where the data are modeled by y(i)vertical bar(x(i), t(i)) similar to F (center dot, mu(i)) with mu(i) = H(eta(t(i)) +X-i(t) beta), for some known distribution function F and link function H. It is shown that the estimates of fi are root-n consistent and asymptotically normal. Through a Monte Carlo study, the performance of these estimators is compared with that of the classical ones.

引用

页码：2856 / 2878

页数：23

共 24 条

[1]

Robust estimators in semiparametric partly linear regression models [J].