Title of article :
Sparse minimum average variance estimation through signal extraction approach to multivariate regression
Author/Authors :
Ahmed, Abdulqader Department of Statistics - College of Administration and Economics - University of Baghdad, Baghdad, Iraq , Mohammad, Saja Department of Statistics - College of Administration and Economics - University of Baghdad, Baghdad, Iraq
Abstract :
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.
Keywords :
High dimensional predictors , Dimension reduction , sparse , Minimum average variance estimation , Signal extraction approach to multivariate regression
Journal title :
International Journal of Nonlinear Analysis and Applications
