Seminar by Jonathan Stallrich

Please Note: This seminar will be held in-person.

Tuning Parameter Selection for Variable Selection via R²

Many penalized estimators are capable of performing simultaneous variable selection and estimation, but are burdened by tuning parameter selection. Many tuning parameter selection procedures tend to choose more variables than necessary and are computationally expensive. We propose a tuning parameter selection strategy based on the squared correlations between the observed response and the predicted values of models, rather than squared error loss. Tuning parameters selected under our procedure are shown to better balance predictive capability and model simplicity. The approach is computationally efficient and, in the domain of penalized estimation, competitive with popular tuning parameter selection techniques in its capacity for variable selection. We explore the efficacy of this approach in a project involving optimal EMG placement for a robotic prosthesis controller.