title: Empirical Likelihood based Inference for Additive Partial Linear Measurement Error Models.
abstract: 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.
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York 14642, U.S.A hliang@bst.rochester.edu.
authors.forename: Hua
authors.initials: H
authors.lastname: Liang
authors.forename: Haiyan
authors.initials: H
authors.lastname: Su
authors.forename: Sally W
authors.initials: SW
authors.lastname: Thurston
authors.forename: John D
authors.initials: JD
authors.lastname: Meeker
authors.forename: Russ
authors.initials: R
authors.lastname: Hauser