On robust classification using projection depth

DOI10.1007/s10463-011-0324-yzbMath1237.62080OpenAlexW2034803504MaRDI QIDQ421427

Subhajit Dutta, Anil Kumar Ghosh

Publication date: 23 May 2012

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10463-011-0324-y

zbMATH Keywords

bandwidthcross-validationkernel density estimationBayes riskmisclassification ratedata depthelliptic symmetrymulti-scale smoothing

Mathematics Subject Classification ID

Related Items (20)

On maximum depth classifiers: depth distribution approach ⋮ Approximate computation of projection depths ⋮ Depth-weighted Bayes classification ⋮ Simulated annealing for higher dimensional projection depth ⋮ Computing the halfspace depth with multiple try algorithm and simulated annealing algorithm ⋮ Fast \(DD\)-classification of functional data ⋮ Multivariate and functional classification using depth and distance ⋮ Distance-based directional depth classifiers: a robustness study ⋮ On some classifiers based on multivariate ranks ⋮ Asymptotics of generalized depth-based spread processes and applications ⋮ DD-Classifier: Nonparametric Classification Procedure Based onDD-Plot ⋮ Fast nonparametric classification based on data depth ⋮ On data depth in infinite dimensional spaces ⋮ Multivariate Functional Halfspace Depth ⋮ RR-classifier: a nonparametric classification procedure in multidimensional space based on relative ranks ⋮ Adaptive exponential power depth with application to classification ⋮ From Depth to Local Depth: A Focus on Centrality ⋮ On rank distribution classifiers for high-dimensional data ⋮ Nonparametrically consistent depth-based classifiers ⋮ Simplified simplicial depth for regression and autoregressive growth processes

Uses Software

• fminsearch
• AS 307

Cites Work

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