【摘 要】
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In this paper, we propose a bias-corrected empirical likelihood (BCEL) for the divergiug number of parameters in a varying coefficient partially linear mode
【机 构】
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HongKongBaptistUniversity
【出 处】
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IMS-China International Conference on Statistics and Probabi
论文部分内容阅读
In this paper, we propose a bias-corrected empirical likelihood (BCEL) for the divergiug number of parameters in a varying coefficient partially linear model.Even when plug-in estimation for nonparametric coefficient functions are used, the BCEL ratio is still asymptotically normal.Thus, it can be dircctly used, without using any other point estimation, to construct confidence region for the parameters of interest.The regularity conditions on the model are very mild: other than the nsual regularity conditions on the smoothness and nonparametric estimation of involved nouparametric functions, only moment conditions on the covariates and error are required.Also the quasi-likelihood framework, least favorable curve, higher order kernel and under smoothing are avoided.Furthermore, the diverging rate of the number of the parameters can be faster than those in the literature.A simulation study is carried out to assess the performance of the proposed method and to compare it with the profile least squares method.A real dataset is analysed for illustration.
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