Regularized MAVE through the elastic net with correlated predictors

Autor:	E Rahman, Ali Alkenani
Rok vydání:	2021
Předmět:	Elastic net regularization History Mathematical analysis Computer Science Applications Education Mathematics
Zdroj:	Journal of Physics: Conference Series. 1897:012018
ISSN:	1742-6596 1742-6588
Popis:	In this article, we proposed a model-free variable selection method (SMAVE-EN). The concepts of sufficient dimension reduction (SDR) and regularization methods are combined to introduce SMAVE-EN. This method is proposed to produce a shrinkage estimation when the predictors are highly correlated under SDR settings. The advantage of SMAVE-EN is that SMAVE-EN extended Elastic net (EN) to nonlinear and multi-dimensional regression under SDR settings. From another side, the SMAVE-EN enables MAVE to work with problems were the predictors are highly correlated. In addition, SMAVE-EN can exhaustively estimate dimensions, while selecting informative covariates simultaneously under SDR framework. The effectiveness of SMAVE-EN is evaluated by both simulation and real data analysis.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::422d8b8a9accc2f8cb32c912aa509ce9 https://doi.org/10.1088/1742-6596/1897/1/012018 Zobrazit plný text záznamu