Fontana, J. (STAT) – When We’re Always Wrong: Scalable Variable Selection in M-Open Settings
A ubiquitous task in statistical practice is that of variable selection, identifying which of a large set of features are the relevant ones. As data sets with a large number of observations have become increasingly common, new theoretical and computational challenges for model selection have emerged. We consider the variable selection problem for linear models […]