Efficiency of profile likelihood in semi-parametric models

被引：3

作者：

Hirose, Yuichi ^{[1
]}

机构：

[1] Victoria Univ Wellington, Sch Math Stat & Operat Res, Wellington, New Zealand

来源：

ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS | 2011年 / 63卷 / 06期

关键词：

Semi-parametric model; Profile likelihood; Two-phase outcome-dependent sampling; Efficiency; M-estimator; Maximum likelihood estimator; Efficient score; Efficient information bound; LARGE-SAMPLE THEORY; MAXIMUM-LIKELIHOOD; LOGISTIC-REGRESSION; 2-PHASE; CONSISTENCY; INFORMATION; ESTIMATORS;

D O I：

10.1007/s10463-010-0280-y

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：

Profile likelihood is a popular method of estimation in the presence of an infinite-dimensional nuisance parameter, as the method reduces the infinite-dimensional estimation problem to a finite-dimensional one. In this paper we investigate the efficiency of a semi-parametric maximum likelihood estimator based on the profile likelihood. By introducing a new parametrization, we improve on the seminal work of Murphy and van der Vaart (J Am Stat Assoc, 95: 449-485, 2000): our improvement establishes the efficiency of the estimator through the direct quadratic expansion of the profile likelihood, which requires fewer assumptions. To illustrate the method an application to two-phase outcome-dependent sampling design is given.

引用

页码：1247 / 1275

页数：29

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