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A variant of sparse partial least squares for variable selection and data exploration
Published Web Location
http://europepmc.org/articles/PMC3939647?pdf=renderNo data is associated with this publication.
Abstract
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
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