Adaptive Bayesian SLOPE : Model Selection With Incomplete Data
We consider the problem of variable selection in high-dimensional settings with missing observations among the covariates. To address this relatively understudied problem, we propose a new synergistic procedure—adaptive Bayesian SLOPE with missing values—which effectively combines SLOPE (sorted l 1 regularization) with the spike-and-slab LASSO (SSL) and is accompanied by an efficient stochastic ap