Sparse sliced inverse regression based on adaptive lasso penalty

In this article, a method of variable selection (V.S) in the concept of sufficient dimension reduction (SDR), called SSIR-AL, is proposed. The SSIR-AL combines the ideas of adaptive Lasso with sliced inverse regression (SIR) to get a sparse SIR estimator. On other hand, the SSIR-AL method enables adaptive lasso to work with multi-dimensional and nonlinear regression without assuming any specific model. Through each of the simulation and analysis of real data was to prove the effectiveness of (SSIR-AL)