A0920
Title: Autotune: Fast, efficient and automatic tuning parameter selection for LASSO
Authors: Sumanta Basu - Cornell University (United States) [presenting]
Abstract: Tuning parameter selection for penalized regression methods such as LASSO is an important issue in practice, albeit less explored in the literature on statistical methodology. Most common choices include cross-validation (CV), which is computationally expensive, or information criteria such as AIC or BIC, which are known to perform worse in high-dimensional scenarios. Guided by the asymptotic theory of LASSO that connects the choice of tuning parameter to the estimation of error standard deviation, autotune is proposed, a procedure that alternately maximizes a penalized log-likelihood over regression coefficients and the nuisance parameter, resulting in an automatic tuning algorithm. Using simulated and real data sets, it is shown that autotune is faster and provides superior estimation, variable selection, and prediction performance than existing tuning strategies for LASSO, as well as alternatives such as the scaled LASSO.
