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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 | ||
| تاریخ دریافت: 04 فروردین 1400، تاریخ پذیرش: 08 خرداد 1400 | ||
| 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 | ||
| مراجع | ||
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