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.
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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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Wang, QH., Jing, BY. Empirical likelihood for partial linear models. Ann Inst Stat Math 55, 585–595 (2003). https://doi.org/10.1007/BF02517809
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DOI: https://doi.org/10.1007/BF02517809