Unveiling the unusual: a task view for anomaly dection in R

P. Talagala
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Abstract

Anomalies play a critical role in statistical analysis, as their presence in data can lead to biased parameter estimation, model misspeci cation, and misleading results if classical analysis techniques are blindly applied. Additionally, anomalies can themselves be carriers of signi cant and critical information, and identifying these critical points can be the primary goal of investigations in many elds such as fraud detection, object tracking, system health monitoring, and environmental monitoring (e.g., for bush res, tsunamis, oods, earthquakes, and volcanic eruptions)
揭示异常:R中异常检测的任务视图
异常在统计分析中起着至关重要的作用,因为如果盲目应用经典分析技术,它们在数据中的存在会导致参数估计有偏差,模型错配和误导性结果。此外,异常本身可以是重要和关键信息的载体,并且识别这些关键点可以成为许多领域调查的主要目标,例如欺诈检测、对象跟踪、系统健康监测和环境监测(例如,丛林、海啸、食品、地震和火山爆发)。
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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