成分数据的无监督异常值检测

IF 0.7 4区数学 Q3 STATISTICS & PROBABILITY

Statistics & Probability Letters Pub Date : 2025-08-06 DOI:10.1016/j.spl.2025.110510

Fabio Divino , Salme Kärkkäinen , Antonello Maruotti

引用次数: 0

摘要

本文提出了一种无监督的成分数据异常点检测方法，该方法通过对受污染的正态分布进行适当的变换和建模。使用期望-条件-最大化算法估计参数。仿真研究和实际数据应用表明，与现有的三种替代方法相比，该方法具有鲁棒性和优越的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Unsupervised outlier detection for compositional data

This paper proposes an unsupervised method for outlier detection in compositional data via an appropriate transformation and modeling with a contaminated normal distribution. Parameters are estimated using an Expectation-Conditional-Maximization algorithm. Simulation studies and real-data applications demonstrate the method’s robustness and superior performance compared to three existing alternatives.

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来源期刊

Statistics & Probability Letters 数学-统计学与概率论

CiteScore

1.60

自引率

0.00%

发文量

173

审稿时长

6 months

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