The rate of convergence for sparse and low-rank quantile trace regression

DOI10.1016/j.jco.2023.101778OpenAlexW4381194315MaRDI QIDQ6084393

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Publication date: 30 November 2023

Published in: Journal of Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jco.2023.101778

zbMATH Keywords

convergence ratelow rankmatrix covariatesquantile trace regressionrow (column) sparsity

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Cites Work

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• Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion
• A tail inequality for suprema of unbounded empirical processes with applications to Markov chains
• Trace regression model with simultaneously low rank and row(column) sparse parameter
• Sparse trace norm regularization
• Generalized high-dimensional trace regression via nuclear norm regularization
• Regression Quantiles
• High-Dimensional Probability
• Regularized Matrix Regression
• Model Selection and Estimation in Regression with Grouped Variables
• A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers

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