<div class="csl-bib-body">
<div class="csl-entry">Oguamalam, J., Radojičić, U., & Filzmoser, P. (2024). Minimum Regularized Covariance Trace Estimator and Outlier Detection for Functional Data. <i>Technometrics</i>, <i>66</i>(4), 588–599. https://doi.org/10.1080/00401706.2024.2336542</div>
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dc.identifier.issn
0040-1706
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/206006
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dc.description.abstract
We propose the Minimum Regularized Covariance Trace (MRCT) estimator, a novel method for robust covariance estimation and functional outlier detection designed primarily for dense functional data. The MRCT estimator employs a subset-based approach that prioritizes subsets exhibiting greater centrality based on the generalization of the Mahalanobis distance, resulting in a fast-MCD type algorithm. Notably, the MRCT estimator handles high-dimensional datasets without the need for preprocessing or dimension reduction techniques, due to the internal smoothening whose amount is determined by the regularization parameter 𝛼>0. The selection of α is automated. An extensive simulation study demonstrates the efficacy of the MRCT estimator in terms of robust covariance estimation and automated outlier detection, emphasizing the balance between noise exclusion and signal preservation achieved through appropriate selection of α. The method converges fast in practice and performs favorably when compared to other functional outlier detection methods.
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dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
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dc.language.iso
en
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dc.publisher
TAYLOR & FRANCIS INC
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dc.relation.ispartof
Technometrics
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dc.subject
Functional outlier detection
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dc.subject
Mahalanobis distance
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dc.subject
Regularization
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dc.subject
Robust covariance estimator
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dc.title
Minimum Regularized Covariance Trace Estimator and Outlier Detection for Functional Data