Kristoffer H. Hellton: Penalized angular regression for personalized predictions
Kristoffer H. Hellton (Department of Statistical Analysis, Machine Learning and Image Analysis, Norwegian Computing Center) will give a talk on January 28th at 14:15 in the Erling Sverdrups plass, Niels Henrik Abels hus, 8th floor.
Kristoffer H. Hellton is a senior research scientist at the Norwegian Computing Center
Title: Penalized angular regression for personalized predictions
Abstract: Personalization is becoming an important feature in many predictive applications. We introduce a penalized regression method implementing personalization inherently in the penalty. Personalized angle (PAN) regression constructs regression coefficients that are specific to the covariate vector for which one is producing a prediction, thus personalizing the regression model itself. This is achieved by penalizing the angles in a hyperspherical parametrization of the regression coefficients. Using a parametric bootstrap procedure to select the tuning parameter, simulations show that PAN regression can outperform ordinary least squares and ridge regression in terms of prediction error. We further prove that by combining the PAN penalty with an L2 penalty the resulting method will have uniformly smaller mean squared prediction error than ridge regression, asymptotically. Finally, we demonstrate the method in a medical application.
Download the flyer here.