Interpretation of partial least-squares regression models with VARIMAX rotation

被引：16
|
作者：
Wang, HW
Liu, Q
Tu, YP
机构：
[1] Beihang Univ, Sch Econ & Management, Beijing 100083, Peoples R China
[2] Beijing Univ, Sch Mech Engn, Beijing 100083, Peoples R China
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2005年 / 48卷 / 01期
基金：
中国国家自然科学基金;
关键词：
partial least-squares regression; factor subspace; VARIMAX rotation; factor analysis;
D O I：
10.1016/j.csda.2003.12.005
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The VARIMAX rotation for factor analysis is used to orthogonally transform the factor subspace, resulting from partial least-square regression (PLSR). If the factors are nearly orthogonal, the transformation may help to interpret the physical meaning of each factor without altering the results of a PLSR model. A case study shows that after the VARIMAX rotation, the loading matrix satisfies "the simple structure criterion" and improves its explanatory ability. (C) 2003 Elsevier B.V. All rights reserved.
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页码：207 / 219
页数：13
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