Usage of the GO estimator in high dimensional linear models

DOI10.1007/S00180-020-01001-2zbMath1505.62156OpenAlexW3036230742MaRDI QIDQ1995832

Murat Genç, M. Revan Özkale

Publication date: 25 February 2021

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-020-01001-2

zbMATH Keywords

variable selectionshrinkageLassocoordinate descent algorithmelastic netgrouping property

Mathematics Subject Classification ID

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

Related Items (1)

SLASSO: a scaled LASSO for multicollinear situations

Uses Software

• glmnet
• ElemStatLearn

Cites Work

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• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Least angle regression. (With discussion)
• Pathwise coordinate optimization
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• Ideal spatial adaptation by wavelet shrinkage
• Sparsity and Smoothness Via the Fused Lasso
• Pruning variable selection ensembles
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• The Restricted and Unrestricted Two-Parameter Estimators
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

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