Depth estimators and tests based on the likelihood principle with application to regression

DOI10.1016/j.jmva.2004.06.006zbMath1065.62085OpenAlexW2025466105MaRDI QIDQ558064

Christine H. Müller

Publication date: 30 June 2005

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2004.06.006

zbMATH Keywords

Poisson distributionGeneralized linear modelsLogistic regressionRegression depthExponential distributionDegenerated \(U\)-statisticDistribution-free testsLikelihood depthPolynomial regressionSimplicial depthSpectral decomposition

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05) Generalized linear models (logistic models) (62J12)

Related Items (17)

Robust estimators and tests for bivariate copulas based on likelihood depth ⋮ A weighted localization of halfspace depth and its properties ⋮ Depth notions for orthogonal regression ⋮ Sign depth tests in multiple regression ⋮ Depth level set estimation and associated risk measures ⋮ Simple powerful robust tests based on sign depth ⋮ Data depth for simple orthogonal regression with application to crack orientation ⋮ Consistency and robustness of tests and estimators based on depth ⋮ Analysis of crack growth with robust, distribution-free estimators and tests for non-stationary autoregressive processes ⋮ New robust tests for the parameters of the Weibull distribution for complete and censored data ⋮ K-sign depth: from asymptotics to efficient implementation ⋮ Tests for multiple regression based on simplicial depth ⋮ Distribution-free tests for polynomial regression based on simplicial depth ⋮ Calculation of simplicial depth estimators for polynomial regression with applications ⋮ Tests Based on Simplicial Depth for AR(1) Models With Explosion ⋮ Nonparametric depth-based multivariate outlier identifiers, and masking robustness properties ⋮ Simplified simplicial depth for regression and autoregressive growth processes

Uses Software

• AS 153

Cites Work

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• Regression Depth
• A Quality Index Based on Data Depth and Multivariate Rank Tests
• Computing the distribution of quadratic forms in normal variables
• Measuring overlap in binary regression.

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