ROBUSTIFYING AND SIMPLIFYING HIGH-DIMENSIONAL REGRESSION WITH APPLICATIONS TO YEARLY STOCK RETURN AND TELEMATICS DATA

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

Abstract The availability of many variables with predictive power 53-264817 makes their selection in a regression context difficult.This study considers robust and 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 cr

read more