Efficient estimation in conditional single-index regression

被引：63

作者：

Delecroix, M

Härdle, W

Hristache, M

机构：

[1] ENSAI, F-35170 Bruz, France

[2] CREST, F-35170 Bruz, France

[3] Humboldt Univ, Inst Stat & Okonometrie, D-10178 Berlin, Germany

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 2003年 / 86卷 / 02期

关键词：

single-index model; pseudo-maximum likelihood; semiparametric efficiency bound;

D O I：

10.1016/S0047-259X(02)00046-5

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：

Semiparametric single-index regression involves an unknown finite-dimensional parameter and an unknown (link) function. We consider estimation of the parameter via the pseudo-maximum likelihood method. For this purpose we estimate the conditional density of the response given a candidate index and maximize the obtained likelihood. We show that this technique of adaptation yields an asymptotically efficient estimator: it has minimal variance among all estimators. (C) 2003 Elsevier Science (USA). All rights reserved.

引用

页码：213 / 226

页数：14

共 18 条

[1]

Bickel Peter J, 1993, Efficient and adaptive estimation for semiparametric models, V4

[2] ON ADAPTIVE ESTIMATION [J].