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

	Sparse minimum average variance estimation through signal extraction approach to multivariate regression
International Journal of Nonlinear Analysis and Applications
مقاله 99، دوره 13، شماره 1، خرداد 2022، صفحه 1167-1173 اصل مقاله (353.89 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.5660
نویسندگان
Abdulqader Ahmed^* ؛ Saja Mohammad
Department of Statistics, College of Administration and Economics, University of Baghdad, Baghdad, Iraq
چکیده
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.
کلیدواژه‌ها
High dimensional predictors؛ Dimension reduction؛ sparse؛ Minimum average variance estimation؛ Signal extraction approach to multivariate regression
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