Cost-efficient Variable Selection Using Branching LARS
Cost-efficient Variable Selection Using Branching LARS
复制标题
使用分支 LARS 进行经济高效的变量选择
DOI：
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复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Lihua Yue
中科院分区：
文献类型：
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作者：
Lihua Yue
变量选择是统计建模中的一个难点问题。确定经济有效的诊断因素对卫生研究人员非常重要，但大多数变量选择方法没有考虑为预测因素收集数据的成本。统计意义和为统计模型收集数据的成本之间的权衡是我们关注的焦点。发展了一个分支LARS(BLARS)程序，它可以选择和估计重要的预测因子，以建立一个既能预测又能节省成本的模型。BLARS方法是LARS变量选择方法的扩展，考虑了各种因素的代价，采用分枝定界搜索法加快搜索过程。加性成本和非加性成本都将得到解决。将描述实现BLARS的R包BranchLars。我们将展示，通过牺牲用户选择的模型精确度，可以选择“更便宜”的模型。
Variable selection is a difficult problem in statistical model building. Identification of cost efficient diagnostic factors is very important to health researchers, but most variable selection methods do not take into account the cost of collecting data for the predictors. The trade off between statistical significance and cost of collecting data for the statistical model is our focus. A Branching LARS (BLARS) procedure has been developed that can select and estimate the important predictors to build a model not only good at prediction but also cost efficient. BLARS method is an extension of the LARS variable selection method to incorporate various costs of factors, where branch and bound search method is employed to accelerate the search process. Both additive and non-additive costs will be addressed. The R package branchLars which implements BLARS will be described. We will show that a “cheaper” model could be selected by sacrificing a user selected amount of model accuracy.
影响因子：
2.7
作者：
Wang, Hansheng;Li, Runze;Tsai, Chih-Ling
通讯作者：
Tsai, Chih-Ling
评估持续治疗小组对精神疾病和药物滥用患者的使用情况。
DOI：
10.1176/ps.46.7.689
发表时间：
1995
期刊：
Psychiatric services (Washington, D.C.)
影响因子：
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作者：
Teague,GB;Drake,RE;Ackerson,TH
通讯作者：
Ackerson,TH