[1]朱春华,高启兵.非参数回归函数最大值点的BRPA估计的强相合性(英文)[J].南京师范大学学报(自然科学版),2015,38(04):57.
 Zhu Chunhua,Gao Qibing.Strong Consistency of BRPA Estimators for Maximizer of Nonparametric Regression Function[J].Journal of Nanjing Normal University(Natural Science Edition),2015,38(04):57.
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非参数回归函数最大值点的BRPA估计的强相合性(英文)()
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《南京师范大学学报》(自然科学版)[ISSN:1001-4616/CN:32-1239/N]

卷:
第38卷
期数:
2015年04期
页码:
57
栏目:
数学
出版日期:
2015-12-30

文章信息/Info

Title:
Strong Consistency of BRPA Estimators for Maximizer of Nonparametric Regression Function
作者:
朱春华1高启兵2
(1.南京审计学院数学与统计学院,江苏 南京 211815)(2.南京师范大学数学科学学院,江苏 南京 210023)
Author(s):
Zhu Chunhua1Gao Qibing2
(1. School of Mathematics and Statistics,Nanjing Audit University,Nanjing 211815,China)(2. School of Mathematics,Nanjing Normal University,Nanjing 210023,China)
关键词:
BRPA估计非参数回归次序统计量强相合性
Keywords:
BRPA estimatornonparametric regressionorder statisticsstrong consistency
分类号:
O212.1
文献标志码:
A
摘要:
非参数函数的BRPA估计在实际中具有一定的应用价值. 本文在一定条件下获得BRPA估计的强相合性,其推广现有文献的结果. 本文结果通过蒙特卡洛方法验证.
Abstract:
The best-r-point-average(BRPA)estimator of the maximizer of regression function has certain merits in application. The strong consistency of the BRPA estimator is obtained under the certain conditions which extends the existing results. The results are illustrated by Monte-Carlo simulations.

参考文献/References:

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[4]CHEN H,HUANG M N L,HUANG W J. Estimation of the location of the maximum of a regression function using extreme order statistics[J]. J Multivariate Anal,1996,57:191-214.
[5]BAI Z D,HUANG M N L. On the consistency of the best-r-points-average estimator for the maximizer of a nonparametric regression function[J]. Sankhy_a,1999,61:208-217.
[6]BAI Z D,CHENG Z H,WU Y H. Convergence rate of the best-r-point-average estimator for the maximizer of a nonparametric regression function[J]. J Multivariate Anal,2003,84;319-334.
[7]WU Y H,WANG X M. The consistency of BRPA estimation of the maximizer of a multivariate regression function[J]. Chin J of Appl Probab and Stati,2000,16:299-302.

备注/Memo

备注/Memo:
Received data:2015-01-18. 
Foundation item:Supported by National Science Foundation of China(11271193),Humanities and Social Sciences Planning Foundation of Chinese Ministry of Education(11YJA910004),Natural Science Foundation of the Jiangsu Higher Education Institutions of China(13KJD110004)and the Returned Overseas Fund of Nanjing Normal University(2014101XLH0194). 
Corresponding author:Gao Qibing,associate professor,majored in Modern Regression analysis. E-mail:gaoqibing@njnu.edu.cn
更新日期/Last Update: 2015-12-30