Most sufficient dimension reduction methods hinge on the existence of finite moments of the predictor vector, a characteristic which is not necessarily warranted for every elliptically contoured distribution as commonly encountered in practice. Hence, we propose a contour-projection approach,...

We employ Lasso shrinkage within the context of sufficient dimension reduction to obtain a shrinkage sliced inverse regression estimator, which provides easier interpretations and better prediction accuracy without assuming a parametric model. The shrinkage sliced inverse regression approach can...