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 all...

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Bibliographic Details
Main Authors: Megan Jodene Olson Hunt, Lisa eWeissfeld, Robert M Boudreau, Howard eAizenstein, Anne B Newman, Eleanor M Simonsick, Dane R Van Domelen, Fridtjof eThomas, Kristine eYaffe, Caterina eRosano
Format: Article
Language:English
Published: Frontiers Media S.A. 2014-03-01
Series:Frontiers in Neuroinformatics
Subjects:
MRI
network
inference
variable selection
multicollinearity
high-dimensional
Online Access:http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00018/full

Internet

http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00018/full

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