NDLI: Empirical Likelihood based Inference for Additive Partial Linear Measurement Error Models.

Content Provider	Semantic Scholar
Author	Liang, Hua Su, Haiyan Thurston, Sally W. Meeker, John D. Hauser, Russ
Copyright Year	2009
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.
Starting Page	433
Ending Page	443
Page Count	11
File Format	PDF HTM / HTML
DOI	10.4310/sii.2009.v2.n1.a8
PubMed reference number	20161079
Journal	Medline
Volume Number	36
Issue Number	3
Alternate Webpage(s)	http://www.intlpress.com/site/pub/files/_fulltext/journals/sii/2009/0002/0001/SII-2009-0002-0001-a008.pdf
Alternate Webpage(s)	https://doi.org/10.4310/sii.2009.v2.n1.a8
Journal	Statistics and its interface
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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Empirical Likelihood based Inference for Additive Partial Linear Measurement Error Models.