正则化线性模型的有效试验设计
Efficient Experimental Design for Regularized Linear Models
摘要：
正则化线性模型，如Lasso，在统计学习和数据科学中引起了极大的关注。然而，在为正则化线性模型构建有效的数据收集方面，还存在零星的工作。在这项工作中，我们提出了一个试验设计的方法，使用近正交拉丁超立方体设计，以提高变量的选择精度的正则化线性模型。系统的方法来构建这样的设计。通过几个算例说明了该方法的有效性。
英文摘要：
Regularized linear models, such as Lasso, have attracted great attention in statistical learning and data science. However, there is sporadic work on constructing efficient data collection for regularized linear models. In this work, we propose an experimental design approach, using nearly orthogonal Latin hypercube designs, to enhance the variable selection accuracy of the regularized linear models. Systematic methods for constructing such designs are presented. The effectiveness of the proposed method is illustrated with several examples.
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