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Chapitre D'ouvrage Année : 2023

Missing Values in RGCCA: Algorithms and Comparisons

Résumé

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. © 2023, Springer Nature Switzerland AG.
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Dates et versions

hal-04272129 , version 1 (06-11-2023)

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Caroline Peltier, Laurent Le Brusquet, François-Xavier Lejeune, Ivan Moszer, Arthur Tenenhaus. Missing Values in RGCCA: Algorithms and Comparisons. Lăcrămioara Radomir; Raluca Ciornea; Huiwen Wang; Yide Liu; Christian M. Ringle; Marko Sarstedt. State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), Springer International Publishing, pp.9-14, 2023, Springer Proceedings in Business and Economics (SPBE), 978-303134588-3. ⟨10.1007/978-3-031-34589-0_2⟩. ⟨hal-04272129⟩
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