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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Bibliographische Detailangaben
Hauptverfasser: 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: Artigo
Sprache:Inglês
Veröffentlicht: Frontiers Media S.A. 2014
Schlagworte:
Neuroscience
Online Zugang:https://ncbi.nlm.nih.gov/pmc/articles/PMC3939647/
https://ncbi.nlm.nih.gov/pubmed/24624079
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fninf.2014.00018
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