Projection-based outlier detection in functional data

Haojie Ren, Nan Chen and Changliang Zou

Biometrika, 2017, vol. 104, issue 2, 411-423

Abstract: SummaryWe 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.

Keywords: Functional principal component analysis; Least-trimmed-squares estimator; Masking; Reweighting; Robustness; Swamping (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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