Half-space mass: a maximally robust and efficient data depth method

DOI10.1007/s10994-015-5524-xzbMath1341.68150OpenAlexW1065912679MaRDI QIDQ747292

Takashi Washio, Kai Ming Ting, Bo Chen, Gholamreza Haffari

Publication date: 23 October 2015

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-015-5524-x

zbMATH Keywords

robustnessdata depthhalf-space massmass estimation

Mathematics Subject Classification ID

Nonparametric robustness (62G35) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

Affine invariant integrated rank-weighted statistical depth: properties and finite sample analysis ⋮ Lowest probability mass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms

Uses Software

• UCI-ml

Cites Work

• Unnamed Item
• Computational science and its applications -- ICCSA 2011. International conference, Santander, Spain, June 20--23, 2011. Proceedings, Part III
• Local depth
• Some intriguing properties of Tukey's half-space depth
• Breakdown properties of location estimates based on halfspace depth and projected outlyingness
• Multivariate analysis by data depth: Descriptive statistics, graphics and inference. (With discussions and rejoinder)
• General notions of statistical depth function.
• Mass estimation
• The infrared structure of gauge theory amplitudes in the high-energy limit

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