多组随机向量主成分得分的典型相关分析

IF 0.6 Q4 STATISTICS & PROBABILITY
T. Ogura, H. Murakami
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引用次数: 0

摘要

典型相关分析(CCA)通常用于分析两个随机向量的变量之间的相关性。作为CCA的扩展,提出了多集规范相关分析(MCCA)来分析多集随机向量之间的相关性。然而,有时解释MCCA结果可能不如解释CCA结果那么简单。为了解决CCA中的这些困难,提出了在两组主成分(PC)得分之间使用CCA的主成分CCA(PCCA)。我们将这一思想应用于多组PC分数,提出了多组PCCA(MPCCA)。PC根据其包含的信息量按降序排列。因此,只使用顶部的几个PC分数就足够了,而不是使用所有的PC分数。减少PC的数量使解释结果变得容易。我们通过仿真研究和实例验证了MPCCA的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Canonical Correlation Analysis of Principal Component Scores for Multiple-set Random Vectors
Canonical correlation analysis (CCA) is often used to analyze correlations between the variables of two random vectors. As an extension of CCA, multiple-set canonical correlation analysis (MCCA) was proposed to analyze correlations between multiple-set random vectors. However, sometimes interpreting MCCA results may not be as straightforward as interpreting CCA results. Principal CCA (PCCA), which uses CCA between two sets of principal component (PC) scores, was proposed to address these difficulties in CCA. We propose multiple-set PCCA (MPCCA) by applying the idea to multiple-set of PC scores. PCs are ranked in descending order according to the amount of information they contain. Therefore, it is enough to use only a few PC scores from the top instead of using all PC scores. Decreasing the number of PC makes it easy to interpret the result. We confirmed the effectiveness of MPCCA using simulation studies and a practical example.
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来源期刊
CiteScore
1.40
自引率
14.30%
发文量
0
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