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高维模型选择方法综述
  • ISSN号：1002-1566
  • 期刊名称：数理统计与管理
  • 时间：2012.7.7
  • 页码：640-658
  • 分类：O213.2[理学—概率论与数理统计;理学—数学]
  • 作者机构：[1]北京师范大学数学科学学院,北京100875, [2]北卡罗来纳大学教堂山分校统计与运筹系,27510美国, [3]中国科学院数学与系统科学研究院,北京100190
  • 相关基金：国家自然科学基金（70625004,11021161,70933003）的资助
  • 相关项目：随机复杂数据与随机复杂结构的理论方法及其应用
作者： 李根|邹国华|张新雨|
关键词： 高维数据, 模型选择, 惩罚因子, 降维, 调整参数, high dimension, model selection, penalized factor, dimension reduction, tuning parameter.
中文摘要：

模型选择是统计学的热点研究问题。近年来随着数据维数越来越高,传统模型选择方法的应用受到了很多制约。本文着重介绍高维模型选择的新方法,并讨论实现模型选择过程的一个重要环节,即调整参数的选取。最后文章总结归纳了未来可能的研究方向。

英文摘要：

Model selection is an important issue in Statistics. Traditional model-selection methods, however, meet difficulties with the increasing of data dimension. This paper is devoted to the survey of the model selection methods for high-dimensional data. The choice of tuning parameters is also discussed. Some future research directions are provided.

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期刊信息
  • 《数理统计与管理》
  • 北大核心期刊（2011版）
  • 主管单位:中国科协
  • 主办单位:中国现场统计研究会
  • 主编：程维虎
  • 地址：中国科学院应用教学所内
  • 邮编：100190
  • 邮箱：sltj@amt.ac.cn
  • 电话：010-62651341
  • 国际标准刊号：ISSN：1002-1566
  • 国内统一刊号：ISSN：11-2242/O1
  • 邮发代号:82-69
  • 获奖情况:
  • 国内外数据库收录:
  • 日本日本科学技术振兴机构数据库,中国中国人文社科核心期刊,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）,中国北大核心期刊（2000版）
  • 被引量:13661