Statistics for big data: A perspective

被引:19
|
作者
Buhlmann, Peter [1 ]
van de Geer, Sara [1 ]
机构
[1] Swiss Fed Inst Technol, Seminar Stat, Zurich, Switzerland
关键词
Heterogeneity; Lasso; Learning theory; Negative results; Replicability; Reproducibility; SELECTION;
D O I
10.1016/j.spl.2018.02.016
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We look at the role of statistics in data science. Two statisticians, two views. Besides the need of developing appropriate concepts, methodology and algorithms, the first one makes a case for validation and carefully designed simulation studies, while the second one writes that a mathematical underpinning of methods is fundamental. Both views converge to the same point: there should be more room for publishing negative findings. (C) 2018 Elsevier B.V. All rights reserved.
引用
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页码:37 / 41
页数:5
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