文章基本信息

标题：Empirical likelihood based inference for additive partial linear measurement error models
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作者：Russ Hauser ; Hua Liang ; John D. Meeker 等
期刊名称：Statistics and Its Interface
印刷版ISSN：1938-7989
电子版ISSN：1938-7997
出版年度：2009
卷号：2
期号：1
页码：83-90
DOI：10.4310/SII.2009.v2.n1.a8
出版社：International Press
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.
关键词：backfitting; correction-for-attenuation; coverage probability; error-prone; local linear regression; semiparamatric estimation; undersmoothing