Strong Consistency of Log-Likelihood-Based Information Criterion in High-Dimensional Canonical Correlation Analysis
Strong Consistency of Log-Likelihood-Based Information Criterion in High-Dimensional Canonical Correlation Analysis
复制标题
高维典型相关分析中基于对数似然的信息准则的强一致性
DOI：
10.1007/s13171-019-00174-3
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Fujikoshi Yasunori
中科院分区：
文献类型：
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作者：
Oda Ryoya;Yanagihara Hirokazu;Fujikoshi Yasunori
我们考虑基于对数似然信息准则的强相合性，在高维情形下，假设q维随机向量和p维随机向量之间的正则相关分析使得样本大小和维备件大头数/n小于1。一般而言，强相合性是比弱相合性更严格的性质；因此，前者的充分条件并不总是与后者的充分条件一致。对于高维情形，我们得到了基于对数似然信息准则的强相合性的充分条件。证明了几个准则强一致的充分条件与Yanagihara等人得到的弱一致的充分条件相同。(J.多元分析，157，70-86：2017)。
We consider the strong consistency of a log-likelihood-based information criterion in a normality-assumed canonical correlation analysis betweenq- andp-dimensional random vectors for a high-dimensional case such that the sample sizenand number of dimensionspare large butp/nis less than 1. In general, strong consistency is a stricter property than weak consistency; thus, sufficient conditions for the former do not always coincide with those for the latter. We derive the sufficient conditions for the strong consistency of this log-likelihood-based information criterion for the high-dimensional case. It is shown that the sufficient conditions for strong consistency of several criteria are the same as those for weak consistency obtained by Yanagihara et al. (J. Multivariate Anal.157, 70–86: 2017).
多变量分析模型选择信息准则的强一致性
影响因子：
0.2
作者：
R. Nishii;Z. Bai;P. R. Krishnaiah
通讯作者：
P. R. Krishnaiah
影响因子：
6.8
作者：
AKAIKE, H
通讯作者：
AKAIKE, H
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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作者：
Keisuke Fukui
通讯作者：
Keisuke Fukui