Optimal designs in sparse linear models

DOI10.1007/s00184-019-00722-9zbMath1441.62198OpenAlexW2951686317MaRDI QIDQ2303755

Xiangshun Kong, Ming-Yao Ai, Yi-min Huang

Publication date: 5 March 2020

Published in: Metrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00184-019-00722-9

zbMATH Keywords

fast algorithmLasso estimatorglobal minimizersupersaturated designeffect sparsity

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Optimal statistical designs (62K05)

Related Items (1)

Design of c-optimal experiments for high-dimensional linear models

Uses Software

• glasso
• glmnet

Cites Work

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• Optimal discrimination designs
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• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• Square-root lasso: pivotal recovery of sparse signals via conic programming
• An Algorithmic Approach to Constructing Supersaturated Designs
• Scaled sparse linear regression
• A Comparison of Algorithms for Constructing Exact D-Optimal Designs
• Columnwise-Pairwise Algorithms with Applications to the Construction of Supersaturated Designs
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models

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