Copulas, uncertainty, and false discovery rate control

被引:2
作者
Cerquet, Roy [1 ]
Lupi, Claudio [2 ]
机构
[1] Univ Macerata, Dept Econ & Law, Via Crescimbeni 20, I-62100 Macerata, Italy
[2] Univ Molise, Dept Econ, Via F De Sanctis, I-86100 Campobasso, Italy
关键词
Copulas; Uncertainty; Dependent test statistics; Multivariate total positivity of order 2; False discovery rate; Multiple testing; DEPENDENT TEST STATISTICS; POSITIVE DEPENDENCE; BIVARIATE COPULAS; SAMPLE SELECTION; QUASI-COPULAS; ERROR RATE; DISTRIBUTIONS; FAMILY; MODEL; CONSTRUCTION;
D O I
10.1016/j.ijar.2018.06.002
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The false discovery rate (FDR) is a powerful approach to multiple testing. However, dependence among test statistics is critical for FDR control. The way in which this dependence structure is described represents the most prominent source of uncertainty of this statistical theme. Copulas play a relevant role among the techniques used to deal with uncertainty and dependence. This paper contributes to fill an existing gap in the scientific debate by exploring the connections between the literature on FDR and that on copulas. In particular, we aim at attracting the interest of the scientific community on this topic by identifying suitable classes of nonstandard copulas which ensure that FDR control can be attained for dependent test statistics. (C) 2018 Elsevier Inc. All rights reserved.
引用
收藏
页码:105 / 114
页数:10
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