sparse minimum average variance estimation through signal extraction approach to multivariate regression

In this paper, a new sparse method called (mave-sier) is proposed, to introduce mave-sier, we combined the effective sufficient dimension reduction method mave with the sparse method signal extraction approach to multivariate regression (sier). mave-sier has the benefit of expanding the signal extraction method to multivariate regression (sier) to nonlinear and multi-dimensional regression. mave-sier also allows mave to deal with problems which the predictors are highly correlated. mave-sier may estimate dimensions exhaustively while concurrently choosing useful variables. simulation studies confirmed mave-sier performance.