Breaking the (Benford) law: Statistical fraud detection in campaign finance

被引：101

作者：

Cho, Wendy K. Tam

Gaines, Brian J.

机构：

[1] Univ Illinois, Dept Polit Sci, Natl Ctr Supercomp Applicat, Urbana, IL 61801 USA

[2] Univ Illinois, Natl Ctr Supercomp Applicat, Dept Stat, Urbana, IL 61801 USA

来源：

AMERICAN STATISTICIAN | 2007年 / 61卷 / 03期

关键词：

data irregularities; data mining; FEC; first-digit distributions; politics;

D O I：

10.1198/000313007X223496

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：

Benford's law is seeing increasing use as a diagnostic tool for isolating pockets of large datasets with irregularities that deserve closer inspection. Popular and academic accounts of campaign finance are rife with tales of corruption, but the complete dataset of transactions for federal campaigns is enormous. Performing a systematic sweep is extremely arduous; hence, these data are a natural candidate for initial screening by comparison to Benford's distributions.

引用

页码：218 / 223

页数：6

共 19 条

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