A variant of sparse partial least squares for variable selection and data exploration

A variant of sparse partial least squares for variable selection and data exploration

When data are sparse and/or predictors multicollinear, current implementation of sparse partial least squares (SPLS) does not give estimates for non-selected predictors nor provide a measure of inference. In response, an approach termed “all-possible” SPLS is proposed, which fits a SPLS model for al...

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Bibliographic Details
Main Authors: Olson Hunt, Megan J., Weissfeld, Lisa, Boudreau, Robert M., Aizenstein, Howard, Newman, Anne B., Simonsick, Eleanor M., Van Domelen, Dane R., Thomas, Fridtjof, Yaffe, Kristine, Rosano, Caterina
Format: Online
Language:English
Published: Frontiers Media S.A. 2014
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3939647/

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3939647/

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