Generalized Mahalanobis depth in the reproducing kernel Hilbert space
From MaRDI portal
Publication:641773
DOI10.1007/s00362-009-0265-1zbMath1434.62082OpenAlexW1987435080MaRDI QIDQ641773
Qiang Li, Yong Wang, Yi Wu, Chenping Hou, Yong-Gang Hu
Publication date: 25 October 2011
Published in: Statistical Papers (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00362-009-0265-1
Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Characterization and structure theory for multivariate probability distributions; copulas (62H05)
Related Items (6)
Choosing among notions of multivariate depth statistics ⋮ Sign depth tests in multiple regression ⋮ Consistency and robustness of tests and estimators based on depth ⋮ Consistency of the likelihood depth estimator for the correlation coefficient ⋮ New robust tests for the parameters of the Weibull distribution for complete and censored data ⋮ K-sign depth: from asymptotics to efficient implementation
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- On a notion of data depth based on random simplices
- Kernel methods in machine learning
- Data depth based on spatial rank.
- On data depth and distribution-free discriminant analysis using separating surfaces
- On the finite sample breakdown points of redescending \(M\)-estimates of location
- General notions of statistical depth function.
- Clustering and classification based on the L\(_{1}\) data depth
- An Extended Projection Data Depth and Its Applications to Discrimination
- Notions of Limiting P Values Based on Data Depth and Bootstrap
- The multivariate L 1 -median and associated data depth
- A Quality Index Based on Data Depth and Multivariate Rank Tests
- On Maximum Depth and Related Classifiers
This page was built for publication: Generalized Mahalanobis depth in the reproducing kernel Hilbert space
