Depth notions for orthogonal regression

DOI10.1016/j.jmva.2010.06.008zbMath1198.62022OpenAlexW1979854397MaRDI QIDQ604352

Christine H. Müller, Robin Wellmann

Publication date: 10 November 2010

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2010.06.008

zbMATH Keywords

asymptotic testssimplicial depthtangent depthorthogonal regressionglobal depth

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05) Nonparametric estimation (62G05) Generalized linear models (logistic models) (62J12)

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Cites Work

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• Depth estimators and tests based on the likelihood principle with application to regression
• On a notion of data depth based on random simplices
• Distribution-free tests for polynomial regression based on simplicial depth
• Calculation of simplicial depth estimators for polynomial regression with applications
• The deepest regression method
• On depth and deep points: A calculus.
• Tests for multiple regression based on simplicial depth
• On a notion of simplicial depth
• Regression Depth
• Location–Scale Depth
• A Distribution-Free Test for Regression Parameters

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