Empirical likelihood based inference for additive partial linear measurement error models

被引：1

作者：

Liang, Hua ^{[1
]}

Su, Haiyan ^{[1
]}

Thurston, Sally W. ^{[1
]}

Meeker, John D. ^{[2
]}

Hauser, Russ ^{[3
]}

机构：

[1] Univ Rochester, Med Ctr, Dept Biostat & Computat Biol, Rochester, NY 14642 USA

[2] Univ Michigan, Dept Environm Hlth Sci, Ann Arbor, MI 48109 USA

[3] Harvard Univ, Sch Publ Hlth, Dept Environm Hlth, Boston, MA 02115 USA

来源：

STATISTICS AND ITS INTERFACE | 2009年 / 2卷 / 01期

关键词：

Backfitting; Correction-for-attenuation; Coverage probability; Error-prone; Local linear regression; Semiparamatric estimation; Undersmoothing; CONFIDENCE-INTERVALS; REGRESSION; VARIABLES;

D O I：

暂无

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

This paper considers statistical inference for additive partial linear models when the linear covariate is measured with error. To improve the accuracy of the normal approximation based confidence intervals, we develop an empirical likelihood based statistic, which is shown to be asymptotically chi-square distributed. We emphasize the finite-sample performance of the proposed method by conducting simulation experiments. The method is used to analyze the relationship between semen quality and phthalate exposure from an environment study.

引用

页码：83 / 90

页数：8

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