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φ-混合样本密度估计的渐近正态 被引量:3

ASYMPTOTIC NORMALITY OF THE DENSITY ESTIMATOR FOR φ-MIXING SAMPLES
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摘要 在较弱的条件下证明φ-混合样本密度核估计的渐近正态性,此结论较好地改进了林正炎所获的结论. The asymptotic normality of the kernel density estimator for φ mixing samples is proved under the weaker conditions.The result improves the one obtained by Lin Zhengyan.
作者 杨善朝
出处 《广西师范大学学报(自然科学版)》 CAS 1996年第1期18-21,共4页 Journal of Guangxi Normal University:Natural Science Edition
基金 广西区教委科研基金资助项目
关键词 φ-混合样本 密度核估计 渐近正态性 φ-mixing samples kernel density estimator asymptotic normality
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参考文献2

  • 1邵启满.一矩不等式及其应用[J]数学学报,1988(06).
  • 2林正炎.相依样本情形时密度的核估计[J]科学通报,1983(12).

同被引文献18

  • 1杨善朝,李永明.强混合样本下回归加权估计的一致渐近正态性[J].数学学报(中文版),2006,49(5):1163-1170. 被引量:5
  • 2Shah Chao YANG.Maximal Moment Inequality for Partial Sums of Strong Mixing Sequences and Application[J].Acta Mathematica Sinica,English Series,2007,23(6):1013-1024. 被引量:13
  • 3林正炎.相依样本情形时密度的核估计[J].科学通报,1983,28(12):709-713.
  • 4CLARK R M.Nonparamtric estimation of a smooth regression function[J].J Roy Statist Soc:S er B,1997,39:107-113.
  • 5FAN Y.Consistent nonparametric multiple regression for dependent heterogeneous processes:the fixed design case[J].J Multivariate Anal,1990,33:72-88.
  • 6ROUSSAS G G,TRAN L T,INANNIDES D A.Fixed design regression for time series:asymptotic normality[J].J Multivariate Anal,1992,40:162-291.
  • 7YANG Shan-Chao.Uniformly asymptotic normality of the regression weighted estimator for negatively associated samples[J].Statist Probab Lett,2003,62:101-110.
  • 8ROUSSAS G G.Asymptotic normality of random fields of positively and negatively associated processes[J].J Multivariate Anal,1994,50:152-173.
  • 9CHANDA K C. Strong mixing properties of linear stochastic processes[J]. Journal of Applied Probability, 1974,11 (2) :401-408.
  • 10GORODESKII V V. On the strong mixing property for linear sequences[J]. Theory of Probability and Their Appli- cations, 1977,22 (2) : 421-423.

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