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Variable Selection for Screening Experiments.
Quality Technology and Quantitative Management ( IF 2.8 ) Pub Date : 2016-02-09 , DOI: 10.1080/16843703.2009.11673199
Runze Li 1 , Dennis K J Lin
Affiliation  

The first step in many applications of response surface methodology is typically the screening process. Variable selection plays an important role in screening experiments when a large number of potential factors are introduced in a preliminary study. Traditional approaches, such as the best subset variable selection and stepwise deletion, may not be appropriate in this situation. In this paper we introduce a variable selection procedure via penalized least squares with the SCAD penalty. An algorithm to find the penalized least squares solution is suggested, and a standard error formula for the penalized least squares estimate is derived. With a proper choice of the regularization parameter, it is shown that the resulting estimate is root n onsistent and possesses an oracle property; namely, it works as well as if the correct submodel were known. An automatic and data-driven approach was proposed to select the regularization parameter. Examples are used to illustrate the effectiveness of the newly proposed approach. The computer codes (written in MATLAB) to perform all calculation are available through the authors for an automatic data-driven variable selection procedure.



中文翻译:

筛选实验的变量选择。

许多响应面方法应用的第一步通常是筛选过程。当在初步研究中引入大量潜在因素时,变量选择在筛选实验中起着重要作用。传统方法,例如最佳子集变量选择和逐步删除,可能不适用于这种情况。在本文中,我们通过带有 SCAD 惩罚的惩罚最小二乘法介绍了一个变量选择程序。提出了一种寻找惩罚最小二乘解的算法,并推导出了惩罚最小二乘估计的标准误差公式。通过正确选择正则化参数,显示结果估计是根n坚持并拥有预言机属性;也就是说,如果知道正确的子模型,它就可以正常工作。提出了一种自动和数据驱动的方法来选择正则化参数。示例用于说明新提出的方法的有效性。执行所有计算的计算机代码(用 MATLAB 编写)可通过作者获得,用于自动数据驱动的变量选择程序。

更新日期:2016-02-09
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