Consistency of log-likelihood-based information criteria for selecting variables in high-dimensional canonical correlation analysis under nonnormality
Consistency of log-likelihood-based information criteria for selecting variables in high-dimensional canonical correlation analysis under nonnormality
复制标题
非正态下高维典型相关分析中基于对数似然信息准则选择变量的一致性
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Keisuke Fukui
中科院分区：
文献类型：
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作者：
Keisuke Fukui
本文的目的是阐明q维和p维随机向量典型相关分析中基于对数似然的信息准则在维数p较大但不超过样本容量时的相合性条件。虽然观测向量被假设为正态分布，但我们不知道潜在的分布是否真的是正态的。因此，一致性的条件进行评估时，基本分布是不正常的高维渐近框架。AMS 2010科目分类：小学62 H12;中学62 H20
The purpose of this paper is to clarify the conditions for consistency of the loglikelihood-based information criteria in canonical correlation analysis of qand p-dimensional random vectors when the dimension p is large but does not exceed the sample size. Although the vector of observations is assumed to be normally distributed, we do not know whether the underlying distribution is actually normal. Therefore, conditions for consistency are evaluated in a high-dimensional asymptotic framework when the underlying distribution is not normal. AMS 2010 subject classification: Primary 62H12; Secondary 62H20
DOI：
10.1111/j.2044-8317.1984.tb00789.x
发表时间：
1984-01-01
影响因子：
2.6
作者：
BROWNE, MW
通讯作者：
BROWNE, MW