Classification with the pot-pot plot

DOI10.1007/s00362-016-0854-8zbMath1419.62156arXiv1608.02861OpenAlexW2964042883WikidataQ63258584 ScholiaQ63258584MaRDI QIDQ2423197

Oleksii Pokotylo, Karl C. Mosler

Publication date: 21 June 2019

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1608.02861

zbMATH Keywords

potential functionskernel density estimatesbandwidth choice\(DD\)-plot\(\alpha \)-procedure\(DD\alpha \)-classifier\(k\)-nearest-neighbors classification

Mathematics Subject Classification ID

Density estimation (62G07) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Uses Software

• KernSmooth
• LIBSVM
• ddalpha

Cites Work

• Fast nonparametric classification based on data depth
• Robust estimation and classification for functional data via projection-based depth notions
• General notions of statistical depth function.
• Nonparametric and semiparametric models.
• Multivariate L-estimation. (With comments)
• Nonparametrically consistent depth-based classifiers
• The \(\mathrm{DD}^G\)-classifier in the functional setting
• Classifying real-world data with the \(DD\alpha\)-procedure
• From Depth to Local Depth: A Focus on Centrality
• DD-Classifier: Nonparametric Classification Procedure Based onDD-Plot
• Comparison of Smoothing Parameterizations in Bivariate Kernel Density Estimation
• Extrapolative problems in automatic control and the method of potential functions
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