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

dc.contributor.authorTalagala, P
dc.date.accessioned2023-09-20T04:10:30Z
dc.date.available2023-09-20T04:10:30Z
dc.date.issued2023-08
dc.description.abstractAnomalies 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)en_US
dc.identifier.doihttps://doi.org/10.31705/BPRM.v3(1).2023.14en_US
dc.identifier.issn2815-0082en_US
dc.identifier.issue1en_US
dc.identifier.journalBolgoda Plains Research Magazineen_US
dc.identifier.pgnospp. 54-57en_US
dc.identifier.urihttp://dl.lib.uom.lk/handle/123/21434
dc.identifier.volume3en_US
dc.identifier.year2023en_US
dc.language.isoenen_US
dc.titleUnveiling the unusual: a task view for anomaly dection in Ren_US
dc.typeArticle-Full-texten_US

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