An angle-based multivariate functional pseudo-depth for shape outlier detection

DOI10.1016/j.jmva.2015.10.016zbMath1381.62069OpenAlexW2195402436MaRDI QIDQ156472

Sonja Kuhnt, André Rehage, Sonja Kuhnt, André Rehage

Publication date: April 2016

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2015.10.016

zbMATH Keywords

bootstrapfunctional datadata depthrobust estimateshape outlier detection

Mathematics Subject Classification ID

Nonparametric robustness (62G35) Bootstrap, jackknife and other resampling methods (62F40)

Related Items (6)

An introduction to recent advances in high/infinite dimensional statistics ⋮ Local half-region depth for functional data ⋮ Functional outlier detection and taxonomy by sequential transformations ⋮ Component-wise outlier detection methods for robustifying multivariate functional samples ⋮ FUNTA ⋮ Feature extraction for functional time series: theory and application to NIR spectroscopy data

Uses Software

• R
• UCI-ml
• fda (R)
• ElemStatLearn
• fda.usc
• MTS
• MFHD
• sde
• FUNTA
• rFUNTA

Cites Work

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• Evaluation of reproducibility for paired functional data
• Partial linear modelling with multi-functional covariates
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• Robust estimation and classification for functional data via projection-based depth notions
• Functional data analysis
• General notions of statistical depth function.
• Trimmed means for functional data
• Penalized discriminant analysis
• Simplicial band depth for multivariate functional data
• Simulation and inference for stochastic differential equations. With R examples.
• Nonparametric functional data analysis. Theory and practice.
• Multivariate Functional Halfspace Depth
• On the Concept of Depth for Functional Data
• Robustness and Complex Data Structures

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