Depth Measures for Multivariate Functional Data

DOI10.1080/03610926.2012.746368zbMath1347.62093OpenAlexW2070179406WikidataQ61915956 ScholiaQ61915956MaRDI QIDQ4929187

Francesca Ieva, Anna Maria Paganoni

Publication date: 13 June 2013

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2434/233394

zbMATH Keywords

rank testsmultivariate functional datadepth measuresECG signals

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Hypothesis testing in multivariate analysis (62H15) Biomedical imaging and signal processing (92C55)

Related Items (18)

Two-sample tests for multivariate functional data with applications ⋮ Sparse Functional Boxplots for Multivariate Curves ⋮ The \(\mathrm{DD}^G\)-classifier in the functional setting ⋮ Simplicial band depth for multivariate functional data ⋮ Tests for equality of several covariance matrix functions for multivariate functional data ⋮ Wavelets in functional data analysis: estimation of multidimensional curves and their derivatives ⋮ Integrated Depths for Partially Observed Functional Data ⋮ Functional regression models: Some directions of future research ⋮ Data depth for measurable noisy random functions ⋮ Consistency of non-integrated depths for functional data ⋮ Multivariate functional outlier detection ⋮ Discussion of ``Multivariate functional outlier detection by M. Hubert, P. Rousseeuw and P. Segaert ⋮ Directional outlyingness for multivariate functional data ⋮ Depth for curve data and applications ⋮ Component-wise outlier detection methods for robustifying multivariate functional samples ⋮ Process capability vector for multivariate nonlinear profiles ⋮ On projection methods for functional time series forecasting ⋮ One-way MANOVA for functional data via Lawley-Hotelling trace test

Cites Work

• On a notion of data depth based on random simplices
• Robust estimation and classification for functional data via projection-based depth notions
• Depth-based inference for functional data
• Projection-based depth functions and associated medians
• General notions of statistical depth function.
• Trimmed means for functional data
• Multivariate L-estimation. (With comments)
• New nonparametric tests of multivariate locations and scales using data depth
• On the Concept of Depth for Functional Data

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