Variable Selection Procedures in Linear Regression Models with Screening Consistency Property
Variable Selection Procedures in Linear Regression Models with Screening Consistency Property作者机构：First Author Corresponding AuthorShanghai University of Engineering Science Shanghai University of Engineering Science
出 版 物：《International English Education Research》 (国际英语教育研究（英文版）)
年 卷 期：2017年第1期
页 面：34-37页
学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0802[工学-机械工程] 070103[理学-概率论与数理统计] 0701[理学-数学] 080201[工学-机械制造及其自动化]
主 题：variable selection orthogonal matching pursuit high dimensional setup screening consistency
摘 要：There are two fundamental goals in statistical learning： identifying relevant predictors and ensuring high prediction accuracy. The first goal, by means of variable selection, is of particular importance when the true underlying model has a sparse representation. Discovering relevant predictors can enhance the performance of the prediction for the fitted model. Usually an estimate is considered desirable if it is consistent in terms of both coefficient estimate and variable selection. Hence, before we try to estimate the regression coefficients β , it is preferable that we have a set of useful predictors m hand. The emphasis of our task in this paper is to propose a method, in the aim of identifying relevant predictors to ensure screening consistency in variable selection. The primary interest is on Orthogonal Matching Pursuit（OMP）.
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