Treatments of non-metric variables in partial least squares and principal component analysis

Jisu Yoon (Corresponding author), Tatyana Krivobokova

Publications: Contribution to journalArticlePeer Reviewed

Abstract

This paper reviews various treatments of non-metric variables in partial least squares (PLS) and principal component analysis (PCA) algorithms. The performance of different treatments is compared in an extensive simulation study under several typical data generating processes and associated recommendations are made. Moreover, we find that PLS-based methods are to prefer in practice, since, independent of the data generating process, PLS performs either as good as PCA or significantly outperforms it. As an application of PLS and PCA algorithms with non-metric variables we consider construction of a wealth index to predict household expenditures. Consistent with our simulation study, we find that a PLS-based wealth index with dummy coding outperforms PCA-based ones.
Original languageEnglish
Pages (from-to)971-987
Number of pages17
JournalJournal of Applied Statistics
Volume45
Issue number6
Early online date17 Jul 2017
Publication statusPublished - 2018
Externally publishedYes

Austrian Fields of Science 2012

  • 101018 Statistics

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