A Cluster-Based Outlier Detection Scheme for Multivariate Data

• 成果类型: Article
• 署名作者: Jobe, J. Marcus; Pokojovy, Michael
• 署名单位: University System of Ohio; Miami University; University of Konstanz
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.983231
• 发表日期: 2015
• 页码: 1543-1551
• 关键词: location

摘要

Detection power of the squared Mahalanobis distance statistic is significantly reduced when several outliers exist within a multivariate dataset of interest. To overcome this masking effect, we propose a computer-intensive cluster-based approach that incorporates a reweighted version of Rousseeuw's minimum covariance determinant method with a multi-step cluster-based algorithm that initially filters out potential masking points. Compared to the most robust procedures, simulation studies show that our new method is better for outlier detection. Additional real data comparisons are given. Supplementary materials for this article are available online.