Missing Values in RGCCA: Algorithms and Comparisons

Caroline Peltier (), Laurent Brusquet (), François-Xavier Lejeune (), Ivan Moszer and Arthur Tenenhaus ()
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Caroline Peltier: Sorbonne Université
Laurent Brusquet: Université Paris-Saclay
François-Xavier Lejeune: Sorbonne Université
Ivan Moszer: Sorbonne Université
Arthur Tenenhaus: Sorbonne Université

A chapter in State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), 2023, pp 9-14 from Springer

Abstract: Abstract Regularized generalized canonical correlation analysis (RGCCA) is a general statistical framework for multiblock data analysis. However, multiblock data often have missing structure, i.e., data in one or more blocks may be completely unobserved for a sample. In this work, several solutions were investigated to properly handle missing data structures within the framework of RGCCA then compared on simulations.

Keywords: Multiblock; RGCCA; Missing values (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-34589-0_2

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DOI: 10.1007/978-3-031-34589-0_2

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