Relationships Between two Methods for Dealing with Missing Data in Principal Component Analysis

被引：0

作者：

Yoshio Takane

Yuriko Oshima-Takane

机构：

[1] Mcgill University,Department of Psychology

来源：

Behaviormetrika | 2003年 / 30卷 / 2期

关键词：

test equating (TE); missing-data-passive (MDP); homogeneity criterion; (generalized) eigenvalue decomposition; (generalized) singular value decomposition; generalized prediction problem;

D O I：

10.2333/bhmk.30.145

中图分类号：

学科分类号：

摘要：

Missing data arise in virtually all practical data analysis situations. The problem of how to deal with them presents a major challenge to many data analysts. A variety of methods have been proposed to deal with missing data. In this paper we discuss two such proposals for principal component analysis (PCA) and investigate their mutual relationships. One was proposed by Shibayama (1988) for test equating (the TE method), and the other is called missing-data-passive (MDP) approach in homogeneity analysis (Meulman, 1982). The two methods are shown to be essentially equivalent despite their different guises.

引用

页码：145 / 154

页数：9