Inference about the slope in linear regression: an empirical likelihood approach

被引：1

作者：

Mueller, Ursula U. ^{[1
]}

Peng, Hanxiang ^{[2
]}

Schick, Anton ^{[3
]}

机构：

[1] Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA

[2] Indiana Univ Purdue Univ, Dept Math Sci, Indianapolis, IN 46202 USA

[3] SUNY Binghamton, Dept Math Sci, Binghamton, NY 13902 USA

来源：

ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS | 2019年 / 71卷 / 01期

关键词：

Efficiency; Estimated constraint functions; Infinitely many constraints; Maximum empirical likelihood estimator; Missing responses; Missing at random; RESPONSES; FUNCTIONALS;

D O I：

10.1007/s10463-017-0632-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：

We present a new, efficient maximum empirical likelihood estimator for the slope in linear regression with independent errors and covariates. The estimator does not require estimation of the influence function, in contrast to other approaches, and is easy to obtain numerically. Our approach can also be used in the model with responses missing at random, for which we recommend a complete case analysis. This suffices thanks to results by Muller and Schick (Bernoulli 23:2693-2719, 2017), which demonstrate that efficiency is preserved. We provide confidence intervals and tests for the slope, based on the limiting Chi-square distribution of the empirical likelihood, and a uniform expansion for the empirical likelihood ratio. The article concludes with a small simulation study.

引用

页码：181 / 211

页数：31

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