Expectile trace regression via low-rank and group sparsity regularization
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Publication:6192205
DOI10.1080/02331888.2023.2269588OpenAlexW4388290726MaRDI QIDQ6192205
Xiao Hui Liu, Ling Peng, Zeinab Rizk, Pei Wen Xiao, Xiangyong Tan
Publication date: 12 February 2024
Published in: Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02331888.2023.2269588
Cites Work
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- Asymmetric Least Squares Estimation and Testing
- Projected estimation for large-dimensional matrix factor models
- Geoadditive expectile regression
- High-dimensional generalizations of asymmetric least squares regression and their applications
- Estimation of (near) low-rank matrices with noise and high-dimensional scaling
- The Dantzig selector and sparsity oracle inequalities
- Oracle inequalities and optimal inference under group sparsity
- Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion
- Expectile regression for analyzing heteroscedasticity in high dimension
- Trace regression model with simultaneously low rank and row(column) sparse parameter
- Nonlinear expectile regression with application to value-at-risk and expected shortfall estimation
- Quantile trace regression via nuclear norm regularization
- Robust estimation and shrinkage in ultrahigh dimensional expectile regression with heavy tails and variance heterogeneity
- Sparse trace norm regularization
- Generalized high-dimensional trace regression via nuclear norm regularization
- Simultaneous analysis of Lasso and Dantzig selector
- Model selection in semiparametric expectile regression
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- Forecasting Using Principal Components From a Large Number of Predictors
- High-Dimensional Probability
- 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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