Detecting and classifying outliers in big functional data

DOI10.1007/s11634-021-00460-9OpenAlexW3196382025MaRDI QIDQ2103860

Oluwasegun Taiwo Ojo, Carlo Sguera, Antonio Fernández Anta, Rosa Elvira Lillo

Publication date: 9 December 2022

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1912.07287

zbMATH Keywords

functional data analysisoutlier detectionfast-MUODMUODsemifast-MUOD

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Functional data analysis (62R10) Applications of statistics (62P99) Statistical aspects of big data and data science (62R07)

Uses Software

• R
• Rainbow
• fda (R)
• Rcpp
• fda.usc
• fdaoutlier
• adamethods

Cites Work

• Directional outlyingness for multivariate functional data
• Multivariate Functional Data Visualization and Outlier Detection
• Robust archetypoids for anomaly detection in big functional data
• A partial overview of the theory of statistics with functional data
• Multivariate functional outlier detection
• A half-region depth for functional data
• An adjusted boxplot for skewed distributions
• Resistant outlier rules and the non-Gaussian case.
• A topologically valid definition of depth for functional data
• Trimmed means for functional data
• Functional outlier detection and taxonomy by sequential transformations
• Functional data analysis.
• Nonparametric functional data analysis. Theory and practice.
• A comparison of algorithms for the multivariate \(L_1\)-median
• Integrated depth for functional data: statistical properties and consistency
• A Measure of Directional Outlyingness With Applications to Image Data and Video
• Multivariate Functional Halfspace Depth
• On the Concept of Depth for Functional Data

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