Robustifying and simplifying high-dimensional regression with applications to yearly stock return and telematics data

Abstract The availability of many variables with predictive power makes their selection in a regression context difficult.This study considers robust and Pet Supplies understandable low-dimensional estimators as building blocks to improve overall predictive power by optimally combining these building blocks.Our new algorithm is based on generalized

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