Efficient estimation in conditional single-index regression

被引：60
|
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
收藏
页码：213 / 226
页数：14
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