Projection-based outlier detection in functional data

• 成果类型: Article
• 署名作者: Ren, Haojie; Chen, Nan; Zou, Changliang
• 署名单位: Nankai University; National University of Singapore
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asx012
• 发表日期: 2017
• 页码: 411423
• 关键词: identification; regression; boxplots; depth; lts

摘要

We propose a procedure based on a high-breakdown mean function estimator to detect outliers in functional data. The robust estimator is obtained from a clean subset of observations, excluding potential outliers, by minimizing the least-trimmed-squares projection coefficients after functional principal component analysis. A threshold rule based on the asymptotic distribution of the functional score-based distance robustly controls the false positive rate and detects outliers effectively. Further improvement in power can be achieved by adding a one-step reweighting procedure. The finite-sample performance of our method demonstrates satisfactory false positive and false negative rates compared with existing outlier detection methods for functional data.