Group variable selection for relative error regression

DOI10.1016/j.jspi.2016.02.006zbMath1341.62240OpenAlexW2275670178MaRDI QIDQ282894

Yuanyuan Lin, Xianhui Liu, Zhan-Feng Wang

Publication date: 12 May 2016

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2016.02.006

zbMATH Keywords

relative errorgroup variable selectionadaptive penaltymultiplicative regression model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items (4)

H-relative error estimation for multiplicative regression model with random effect ⋮ A novel group VIF regression for group variable selection with application to multiple change-point detection ⋮ A new relative error estimation for partially linear multiplicative model ⋮ A relative error-based estimation with an increasing number of parameters

Cites Work

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• The Adaptive Lasso and Its Oracle Properties
• The log-linear group-lasso estimator and its asymptotic properties
• Least product relative error estimation
• Simultaneous estimation and variable selection in median regression using Lasso-type penalty
• A note on adaptive group Lasso
• Relative-error prediction
• Group variable selection and estimation in the Tobit censored response model
• The Group Lasso for Logistic Regression
• Group variable selection for data with dependent structures
• Regression coefficient and autoregressive order shrinkage and selection via the lasso
• Least Absolute Relative Error Estimation
• Model Selection and Estimation in Regression with Grouped Variables
• Adaptive Lasso for Cox's proportional hazards model

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