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Some uniform convergence results for kernel estimators

Some uniform convergence results for kernel estimators
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摘要 This paper derives some uniform convergence rates for kernel regression of some index functions that may depend on infinite dimensional parameter. The rates of convergence are computed for independent, strongly mixing and weakly dependent data respectively. These results extend the existing literature and are useful for the derivation of large sample properties of the estimators in some semiparametric and nonparametric models. This paper derives some uniform convergence rates for kernel regression of some index functions that may depend on infinite dimensional parameter. The rates of convergence are computed for independent, strongly mixing and weakly dependent data respectively. These results extend the existing literature and are useful for the derivation of large sample properties of the estimators in some semiparametric and nonparametric models.
出处 《Science China Mathematics》 SCIE 2013年第9期1945-1956,共12页 中国科学:数学(英文版)
基金 National Natural Science Foundation of China (Grant No. 70971082) Shanghai Leading Academic Discipline Project at Shanghai University of Finance and Economics (SHUFE) (Grant No. B803) Key Laboratory of Mathematical Economics (SHUFE), Ministry of Education
关键词 uniform convergence kernel estimation convergence rate 一致收敛 核估计 大样本性质 非参数模型 收敛速度 核回归 无穷维 强混合
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参考文献14

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