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山东大学学报(理学版) ›› 2015, Vol. 50 ›› Issue (04): 20-23.doi: 10.6040/j.issn.1671-9352.0.2014.043

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随机缺失下半参数回归模型的最大经验似然估计

武大勇1, 李锋2   

  1. 1. 郑州航空工业管理学院数理系, 河南 郑州 450015;
    2. 郑州航空工业管理学院经贸学院, 河南 郑州 450015
  • 收稿日期:2014-02-20 修回日期:2014-09-24 出版日期:2015-04-20 发布日期:2015-04-17
  • 作者简介:武大勇(1978-),男,硕士,讲师,研究方向为数理统计学及统计学在经济中的应用.E-mail:zzziawdy@163.com
  • 基金资助:
    国家自然科学基金资助项目(U1404104);河南省教育厅科学技术研究重点项目基础研究计划(14A110015)

Maximum empirical likelihood estimation in nonlinear semiparametric regression models with missing data

WU Da-yong1, LI Feng2   

  1. 1. Department of Mathematics and Physics, Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou 450015, Henan, China;
    2. Faculty of Trade and Economics, Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou 450015, Henan, China
  • Received:2014-02-20 Revised:2014-09-24 Online:2015-04-20 Published:2015-04-17

摘要: 考虑了随机缺失数据下非线性回归模型的估计问题,利用最大经验似然估计的方法给出了回归系数、光滑函数的最大经验似然估计,并在一定条件下证明了所得估计量的渐近正态性和强相合性.

关键词: 非线性半参数回归模型, 缺失数据, 渐近正态性, 经验似然, 相合性

Abstract: The linear semiparametric regression models with missing data were considered. The maximum empirical estimations of the regression coefficients, and the smoothing function were obtained by the maximum empirical method. The asymptotic normality and consistency of the proposed estimations were proved under some appropriate conditions.

Key words: nonlinear semiparametric regression models, missing data, empirical likelihood, asymptotic normality, consistency

中图分类号: 

  • O212
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