Shrinkage and model selection with correlated variables via weighted fusion

DOI10.1016/j.csda.2008.11.007zbMath1452.62049OpenAlexW2039530176MaRDI QIDQ961274

Z. John Daye, X. Jessie Jeng

Publication date: 30 March 2010

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

Full work available at URL: https://doi.org/10.1016/j.csda.2008.11.007

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (16)

An extended variable inclusion and shrinkage algorithm for correlated variables ⋮ Sparse Laplacian shrinkage with the graphical Lasso estimator for regression problems ⋮ Grouping Variable Selection by Weight Fused Elastic Net for Multi-Collinear Data ⋮ Generalised regression estimators for average treatment effect with multicollinearity in high-dimensional covariates ⋮ Graph structured sparse subset selection ⋮ Practical variable selection for generalized additive models ⋮ The smooth-Lasso and other \(\ell _{1}+\ell _{2}\)-penalized methods ⋮ Special issue on variable selection and robust procedures ⋮ The sparse Laplacian shrinkage estimator for high-dimensional regression ⋮ Regression adjustment for treatment effect with multicollinearity in high dimensions ⋮ Group variable selection for data with dependent structures ⋮ Penalized regression combining the \( L_{1}\) norm and a correlation based penalty ⋮ The Adaptive Gril Estimator with a Diverging Number of Parameters ⋮ Multivariate sparse Laplacian shrinkage for joint estimation of two graphical structures ⋮ Graph-Based Regularization for Regression Problems with Alignment and Highly Correlated Designs ⋮ Group Variable Selection with Oracle Property by Weight-Fused Adaptive Elastic Net Model for Strongly Correlated Data

Uses Software

• LASSO
• alr3

Cites Work

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• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Ridge Regression: Applications to Nonorthogonal Problems
• Regression modeling strategies. With applications to linear models, logistic regression and survival analysis

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