Matches in SemOpenAlex for { <https://semopenalex.org/work/W2401815844> ?p ?o ?g. }
- W2401815844 abstract "Predictive performance in shrinkage regression suffers from two major difficulties: (i) the amount of relative shrinkage is monotone in the singular values of the design matrix and (ii) the amount of shrinkage does not depend on the response variables. Both of these factors can translate to a poor prediction performance, the risk of which can be explicitly quantified using Stein's unbiased risk estimate. We show that using a component-specific local shrinkage term that can be learned from the data under a suitable heavy-tailed prior, in combination with a global term providing shrinkage towards zero, can alleviate both these difficulties and consequently, can result in an improved risk for prediction. Demonstration of improved prediction performance over competing approaches in a simulation study and in a pharmacogenomics data set confirms the theoretical findings." @default.
- W2401815844 created "2016-06-24" @default.
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- W2401815844 date "2016-05-16" @default.
- W2401815844 modified "2023-09-27" @default.
- W2401815844 title "Prediction risk for global-local shrinkage regression" @default.
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