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随机适应误差下线性模型参数M估计的渐近性质
  • 期刊名称：应用数学学报，2009, 32（6），1123-1132
  • 时间：0
  • 分类：O212.1[理学—概率论与数理统计;理学—数学]
  • 作者机构：[1]北京师范大学统计与金融数学系,北京100875, [2]陕西师范大学数学与信息科学学院,西安710062
  • 相关基金：国家自然科学基金（10771017）和陕西师范大学科研启动基金（No.999309）资助项目.致谢作者衷心感谢审稿人提出的宝贵审稿意见.本文得到崔恒建教授的指导和帮助,谨表谢意.
  • 相关项目：复杂数据分析中的稳健统计方法及其应用
关键词： 线性模型, 随机适应序列, M估计, 强相合性, 渐近正态性, linear model, stochastic adapted sequences, M-estimator, strong consistency asymptotic normality
中文摘要：

在一些较弱的充分条件下，本文研究了误差为随机适应序列下，线性模型回归参数M估计的强相合性．与文献中已有结果比较，扩大了应用范围，且对矩条件也有较大改进．同时我们给出了随机适应误差下线性模型参数M估计的渐近正态性．

英文摘要：

Under some suitable sufficiency conditions, we consider the strong consistency of M estimator of regression parameter in linear model with stochastic adapted errors. This extends several known results. Meanwhile, we obtain the asymptotic normality of M estimator of regression parameter under stochastic adapted errors.

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