Depth-based nonparametric description of functional data, with emphasis on use of spatial depth

DOI10.1016/j.csda.2016.07.007zbMath1466.62192OpenAlexW2489577344MaRDI QIDQ1658519

Uditha Wijesuriya, Robert J. Serfling

Publication date: 14 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2016.07.007

zbMATH Keywords

quantilesnonparametricfunctional dataoutlier detectionboxplot

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Functional data analysis (62R10)

Related Items

Multivariate \(\rho \)-quantiles: a spatial approach, 2nd special issue on robust analysis of complex data, Monotonicity properties of spatial depth, Statistical depth in abstract metric spaces, Nonparametric depth and quantile regression for functional data, Directional outlyingness for multivariate functional data, Flexible quantile contour estimation for multivariate functional data: beyond convexity, On projection methods for functional time series forecasting

Uses Software

• Rainbow
• MNM

Cites Work

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• Efficient and fast estimation of the geometric median in Hilbert spaces with an averaged stochastic gradient algorithm
• Robust estimation and classification for functional data via projection-based depth notions
• Multivariate nonparametric methods with R. An approach based on spatial signs and ranks.
• The random Tukey depth
• Trimmed means for functional data
• Nonparametric multivariate descriptive measures based on spatial quantiles
• Uniform convergence and asymptotic confidence bands for model-assisted estimators of the mean of sampled functional data
• Spatial depth-based classification for functional data
• On masking and swamping robustness of leading nonparametric outlier identifiers for univariate data
• Resistant estimates for high dimensional and functional data based on random projections
• Equivariance and invariance properties of multivariate quantile and related functions, and the role of standardisation
• On a Geometric Notion of Quantiles for Multivariate Data
• Horvitz-Thompson estimators for functional data: asymptotic confidence bands and optimal allocation for stratified sampling
• Robust functional estimation using the median and spherical principal components
• Approximation Theorems of Mathematical Statistics
• Multivariate Functional Halfspace Depth
• On the Concept of Depth for Functional Data
• Using Complex Surveys to Estimate the L₁‐Median of a Functional Variable: Application to Electricity Load Curves