Robust joint modeling of mean and dispersion through trimming

DOI10.1016/j.csda.2011.07.007zbMath1239.62018OpenAlexW2043724780MaRDI QIDQ429614

N. M. Neykov, Peter Filzmoser, Plamen Nikolov Neytchev

Publication date: 20 June 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2011.07.007

zbMATH Keywords

breakdown pointgeneralized linear modelsoutlier detectionextended quasi-likelihoodextended trimmed quasi-likelihoodjoint modeling of mean and dispersion

Mathematics Subject Classification ID

Related Items (10)

Reduced-rank vector generalized linear models with two linear predictors ⋮ Robust methods for heteroskedastic regression ⋮ Mixtures of regression models with incomplete and noisy data ⋮ Risk measures in a quantile regression credibility framework with Fama/French data applications ⋮ Robust Inference and Modeling of Mean and Dispersion for Generalized Linear Models ⋮ The least trimmed quantile regression ⋮ Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator ⋮ Special issue on robust analysis of complex data ⋮ Covariance matrices of S robust regression estimators ⋮ The Use of Working Variables in the Bayesian Modeling of Mean and Dispersion Parameters in Generalized Nonlinear Models with Random Effects

Uses Software

• R
• Statmod
• robustbase
• Tweedie
• dglm
• JointModeling

Cites Work

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