Empirical likelihood for partial linear models |
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Authors: | Qi-Hua Wang Bing-Yi Jing |
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Affiliation: | (1) Academy of Mathematics and System Science, Chinese Academy of Science, 100080 Beijing;(2) China and Heilongjiang University, Harbin, China;(3) Department of Mathematics, Hong Kong University of Science and Technology, Kowloon, Hong Kong, China |
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Abstract: | In this paper the empirical likelihood method due to Owen (1988,Biometrika,75, 237–249) is applied to partial linear random models. A nonparametric version of Wilks' theorem is derived. The theorem is then used to construct confidence regions of the parameter vector in the partial linear models, which has correct asymptotic coverage. A simulation study is conducted to compare the empirical likelihood and normal approximation based method. Research supported by NNSF of China and a grant to the first author for his excellent Ph.D. dissertation work in China. Research supported by Hong Kong RGC CERG No. HKUST6162/97P. |
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Keywords: | Empirical likelihood partial linear model Wilks' theorem |
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