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

Document Type : Research Paper


Department of Statistics, College of Administration and Economics, University of Baghdad, Baghdad, Iraq


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.


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Volume 13, Issue 1
March 2022
Pages 1167-1173
  • Receive Date: 24 March 2021
  • Accept Date: 29 May 2021