An improved asymptotic test for the Jaccard similarity index for binary data

被引：8
|
作者：
Koeneman, Scott H. H. [1 ]
Cavanaugh, Joseph E. E. [1 ]
机构：
[1] Univ Iowa, Coll Publ Hlth, Dept Biostat, 145 N Riverside Dr, Iowa City, IA 52242 USA
来源：
STATISTICS & PROBABILITY LETTERS | 2022年 / 184卷
关键词：
Association measures; Bernoulli distribution; Binary data; Jaccard index; Multinomial distribution;
D O I：
10.1016/j.spl.2022.109375
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：
For paired binary data, we propose a new asymptotic test of independence for the Jaccard index. As demonstrated, the test offers marked improvements in maintaining nominal Type I error rates, and exhibits higher power when these error rates are comparable. (c) 2022 Elsevier B.V. All rights reserved.
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页数：5
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