A Robust Variable Selection tot-type Joint Generalized Linear Models via Penalizedt-type Pseudo-likelihood

DOI10.1080/03610918.2014.901358zbMath1346.62049OpenAlexW2143433075MaRDI QIDQ2821000

Zhong-Zhan Zhang, Guo-Liang Tian, Deng-Ke Xu, Liu-Cang Wu

Publication date: 16 September 2016

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2014.901358

zbMATH Keywords

variable selection\(t\)-type pseudo-likelihoodjoint generalized linear modelspenalized maximum \(t\)-type pseudo-likelihood estimator

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Generalized linear models (logistic models) (62J12)

Related Items (3)

Variable Selection in Heteroscedastic Regression Models Under General Skew-t Distributional Models Using Information Complexity ⋮ Estimation and variable selection for mixture of joint mean and variance models ⋮ Robust rank-based variable selection in double generalized linear models with diverging number of parameters under adaptive Lasso

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