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[BSA19] A generalization of partial least squares regression and correspondence analysis for categorical and mixed data: An application with the ADNI data

Autres :
datepub: October 2019,
Pages: 1-51,

Mots clés: PLS regression, correspondence analysis

Résumé: The present and future of large scale studies of human brain and behavior—in typical and disease populations—is “mutli-omics” and “deep-phenotyping”. These studies rely on highly interdisciplinary teams that collect extremely diverse types of data across numerous systems and scales of measurement (e.g., genetics, brain structure, and behavior). Such large, complex, and heterogeneous data requires relatively simple methods that allow for flexibility in analyses without the loss of the inherent properties of various data types. Here we introduce a method specifically designed for these problems: partial least squares-correspondence analysis-regression (PLS-CA-R). PLS-CA-R generalizes PLS regression for use with virtually any data type (e.g., continuous, ordinal, categorical, non-negative values), and more broadly generalizes many of the routine “two-table” multivariate techniques such as various PLS approaches, canonical correlation analysis, and redundancy analysis (a.k.a. reduced rank regression).

Commentaires: preprint BioArXiv : https://www.biorxiv.org/content/10.1101/598888v2

BibTeX

@misc {
BSA19,
title="{A generalization of partial least squares regression and correspondence analysis for categorical and mixed data: An application with the ADNI data}",
author="D. Beaton and G. Saporta and H. Abdi",
year=2019,
note="{preprint BioArXiv : https://www.biorxiv.org/content/10.1101/598888v2}",
}