论文标题
平滑测试Newcomb-Benford分销的合适性测试
Smooths Tests of Goodness-of-fit for the Newcomb-Benford distribution
论文作者
论文摘要
Newcomb-Benford的概率分布在许多领域使用统计数据变得非常流行，尤其是在欺诈检测中。在这种情况下，重要的是要确定数据集是否来自此分布，同时控制了类型1错误的风险，即错误地识别欺诈行为和类型2错误，即未检测到发生欺诈。完成这项工作的统计工具是一个合适性测试。对于Newcomb-Benford的发行版，最受欢迎的测试是Pearson的卡方测试，其功率与2型错误相关，已知很弱。因此，最近引入了其他测试。本工作的目的是根据平稳的测试原理为此分布建立新的合适性测试。然后将这些测试与一些竞争对手进行比较。事实证明，本文的建议在全球范围内比现有测试更可取，在欺诈检测环境中应认真考虑。
The Newcomb-Benford probability distribution is becoming very popular in many areas using statistics, notably in fraud detection. In such contexts, it is important to be able to determine if a data set arises from this distribution while controlling the risk of a Type 1 error, i.e. falsely identifying a fraud, and a Type 2 error, i.e. not detecting that a fraud occurred. The statistical tool to do this work is a goodness-of-fit test. For the Newcomb-Benford distribution, the most popular such test is Pearson's chi-square test whose power, related to the Type 2 error, is known to be weak. Consequently, other tests have been recently introduced. The goal of the present work is to build new goodness-of-fit tests for this distribution, based on the smooth test principle. These tests are then compared to some of their competitors. It turns out that the proposals of the paper are globally preferable to existing tests and should be seriously considered in fraud detection contexts, among others.
