Using Classification and Regression Trees (CART) and random forests to analyze attrition: Results from two simulations.
Using Classification and Regression Trees (CART) and random forests to analyze attrition: Results from two simulations.
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DOI：
10.1037/pag0000046
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发表时间：
2015-12
影响因子：
3.7
通讯作者：
McArdle JJ
中科院分区：
文献类型：
--
作者：
Hayes T;Usami S;Jacobucci R;McArdle JJ
在这篇文章中，我们描述了磨耗分析的最新发展：使用分类和回归树（CART）和随机森林方法来生成逆采样权值。这些灵活的机器学习技术有可能捕获复杂的非线性、交互式选择模型，但据我们所知，它们在缺失数据分析环境中的表现从未被评估过。为了评估这些方法的潜在好处，我们在2个模拟中比较了它们与常用的多重imputation和完整案例技术的性能。这些初步结果表明，与其他方法相比，从修剪后的CART分析计算的权重在偏差和效率方面都表现良好。我们讨论了这些发现对应用研究人员的意义。
In this article, we describe a recent development in the analysis of attrition: using classification and regression trees (CART) and random forest methods to generate inverse sampling weights. These flexible machine learning techniques have the potential to capture complex nonlinear, interactive selection models, yet to our knowledge, their performance in the missing data analysis context has never been evaluated. To assess the potential benefits of these methods, we compare their performance with commonly employed multiple imputation and complete case techniques in 2 simulations. These initial results suggest that weights computed from pruned CART analyses performed well in terms of both bias and efficiency when compared with other methods. We discuss the implications of these findings for applied researchers.