Variable selection for high‐dimensional generalized linear model with block‐missing data

DOI10.1111/sjos.12632OpenAlexW4317756973MaRDI QIDQ6049794

Xin-Yuan Song, Yifan He, Yang Feng

Publication date: 11 October 2023

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/sjos.12632

zbMATH Keywords

sparsityLassograph modelinverse covariance matrixblock-wise missingness

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Missing values: sparse inverse covariance estimation and an extension to sparse regression
• Sparse inverse covariance estimation with the graphical lasso
• CoCoLasso for high-dimensional error-in-variables regression
• High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity
• Convex multi-task feature learning
• Sparsistency and rates of convergence in large covariance matrix estimation
• Network exploration via the adaptive LASSO and SCAD penalties
• High-dimensional graphs and variable selection with the Lasso
• Asymptotic Equivalence of Regularization Methods in Thresholded Parameter Space
• Covariance-Regularized Regression and Classification for high Dimensional Problems
• Model selection and estimation in the Gaussian graphical model
• Optimal Sparse Linear Prediction for Block-missing Multi-modality Data Without Imputation
• Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis
• Robust estimation of high-dimensional covariance and precision matrices
• Integrating Multisource Block-Wise Missing Data in Model Selection
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