Prediction, Estimation, and Attribution
Prediction, Estimation, and Attribution
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DOI：
10.1080/01621459.2020.1762613
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发表时间：
2020-04-02
影响因子：
3.7
通讯作者：
Efron, Bradley
中科院分区：
文献类型：
--
作者：
Efron, Bradley
20世纪的科学需求和计算限制塑造了经典的统计方法。在21世纪，需求和限制都发生了变化，方法也发生了变化。大规模的预测算法——神经网络、深度学习、增强、支持向量机、随机森林——已经在大众媒体中获得了明星的地位。它们被认为是回归传统的继承者，但它们是在巨大的规模和庞大的数据集上进行的。这些算法与标准回归技术（如普通最小二乘或逻辑回归）相比如何？几个关键的差异将被检查，集中在预测和估计或预测和归因（显著性检验）之间的差异。大部分的讨论都是通过小的数值例子进行的。
The scientific needs and computational limitations of the twentieth century fashioned classical statistical methodology. Both the needs and limitations have changed in the twenty-first, and so has the methodology. Large-scale prediction algorithms-neural nets, deep learning, boosting, support vector machines, random forests-have achieved star status in the popular press. They are recognizable as heirs to the regression tradition, but ones carried out at enormous scale and on titanic datasets. How do these algorithms compare with standard regression techniques such as ordinary least squares or logistic regression? Several key discrepancies will be examined, centering on the differences between prediction and estimation or prediction and attribution (significance testing). Most of the discussion is carried out through small numerical examples.