Smooth depth contours characterize the underlying distribution

DOI10.1016/j.jmva.2010.06.007zbMath1201.62064OpenAlexW2074132480MaRDI QIDQ990904

Linglong Kong, YiJun Zuo

Publication date: 1 September 2010

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2010.06.007

zbMATH Keywords

halfspace depthcharacterizationdepth contoursmooth contour

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Estimation in multivariate analysis (62H12) Characterization and structure theory for multivariate probability distributions; copulas (62H05)

Related Items (22)

RR-plot: a descriptive tool for regression observations ⋮ Depth-based classification method underlain by a remote concentration measure for processing asymmetric data ⋮ Tukey Depths and Hamilton--Jacobi Differential Equations ⋮ A weighted localization of halfspace depth and its properties ⋮ Some multivariate goodness of fit tests based on data depth ⋮ Weighted lens depth: Some applications to supervised classification ⋮ On smoothness of Tukey depth contours ⋮ Integrated depth for functional data: statistical properties and consistency ⋮ Simple halfspace depth ⋮ Fast implementation of the Tukey depth ⋮ Unnamed Item ⋮ A consistent jackknife empirical likelihood test for distribution functions ⋮ Halfspace depth and floating body ⋮ Detecting a structural change in functional time series using local Wilcoxon statistic ⋮ Reconstruction of atomic measures from their halfspace depth ⋮ The limit of finite sample breakdown point of Tukey's halfspace median for general data ⋮ Local bilinear multiple-output quantile/depth regression ⋮ Precision Index in the Multivariate Context ⋮ Halfspace depth does not characterize probability distributions ⋮ Second-order accuracy of depth-based bootstrap confidence regions ⋮ From Depth to Local Depth: A Focus on Centrality ⋮ Simplified simplicial depth for regression and autoregressive growth processes

Cites Work

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• Probability for Statisticians
• Location–Scale Depth
• A Quality Index Based on Data Depth and Multivariate Rank Tests
• Characterizations of Strong Regularity for Variational Inequalities over Polyhedral Convex Sets
• A Bivariate Sign Test
• The depth function of a population distribution.

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