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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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[1] H.S.K. Chun, Sparse partial least squares regression for simultaneous dimension reduction and variable selection, J. R. Stat. Soc. Ser. B Stat. Meth. (2010) 3–25. [2] R. Cook, Regression Graphics: Ideas for Studying the Regression Through Graphics, New York, Wily, 1998. [3] B.H. Efron, Least angle regression, Ann. Stat. (2004) 407—499. [4] J.A. Fan, Variable selection via non-concave penalized likelihood and its oracle properties, J. Amer. Stat. Assoc. (2001) 1348–1360. [5] H and Z. Hastie, Regularization and variable selection via the elastic net, J. Royal Stat Soci (2005) 1418–142. [6] B. Leo, Better Subset Regression Using the Nonnegative Garrote, Technomet. (1995) 373–384. [7] K. Li, Sliced inverse regression for dimension reduction (with discussion), J. Amer. Stat. Assoc. (1991) 316–342. [8] L.L.-X. Li, Groupwise dimension reduction, J. Amer. Stat. Assoc. (2010) 1188–1201. [9] R. Luo X. Qi, Signal extraction approach for sparse multivariate response regression, J. Multivar. Anal. (2017) 83–97. [10] Q. Wang and X. Yin, A nonlinear multi-dimensional variable selection method for high dimensional data: Sparse MAVE, Comput. Stat. Data Anal. 52(9) (2008) 4512—4520. [11] R. Tibshirani, Regression shrinkage and selection via the Lasso, J. Royal Stat. Soc. (1996) 267–288. [12] T.X. Wang, Penalized minimum average variance estimation, Stat. Sinica (2013) 543–569. [13] T.X. Wang, Variable selection and estimation for semiparametric multiple-index models, Bernoulli 21(2015) 242–275. [14] Y.T. Xia, An adaptive estimation of dimension reduction space, J. Royal Stat. Soc. (2002) 363–410. [15] Yu and K. Alkenani, Sparse MAVE with oracle penalties, Adv. Appl. Stat. (2013) 85—105. [16] H. A. Zou, Regularization and variable selection via the elastic net, J. Royal Stat. Soc. (2005) 301—320. | ||
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