Interpretation of partial least-squares regression models with VARIMAX rotation

Huiwen Wang; Qiang Liu; Yongping Tu

doi:10.1016/j.csda.2003.12.005

Interpretation of partial least-squares regression models with VARIMAX rotation

Huiwen Wang^*
, Qiang Liu
, Yongping Tu

^*此作品的通讯作者

Beihang University

科研成果: 期刊稿件 › 文章 › 同行评审

22 引用（Scopus）

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.

源语言	英语
页（从-至）	207-219
页数	13
期刊	Computational Statistics and Data Analysis
卷	48
期	1
DOI	https://doi.org/10.1016/j.csda.2003.12.005
出版状态	已出版 - 1 1月 2005

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10.1016/j.csda.2003.12.005

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AU - Tu, Yongping

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AB - 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.

KW - Factor analysis

KW - Factor subspace

KW - Partial least-squares regression

KW - VARIMAX rotation

UR - https://www.scopus.com/pages/publications/10144230052

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DO - 10.1016/j.csda.2003.12.005

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Interpretation of partial least-squares regression models with VARIMAX rotation

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