Incorporating Graphical Structure of Predictors in Sparse Quantile Regression
Bibliographic Data
| ID | 19419757 |
|---|---|
| Authors | Zhanfeng Wang (0000-0001-6722-2298, University of Science and Technology of China), Xianhui Liu (0009-0008-7048-7148, Jiangxi University of Finance and Economics), Wenlu Tang (0000-0001-9448-578X, Department of Statistics, The Chinese University of Hong Kong), Yuanyuan Lin (0000-0002-7773-4921, Department of Statistics, The Chinese University of Hong Kong, corresponding author) |
| Year | 2021 |
| Volume | 39 |
| Issue | 3 |
| Pages | 783-792 |
| Publication date | 2021-07-03 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2020.1730859 |
| OpenAlex | W3007450732 |
| Language | EN |
| References cited | 55 |
Quantile regression in high-dimensional settings is useful in analyzing high-dimensional heterogeneous data. In this article, different from existing methods in quantile regression which treat all the predictors equally with the same priori, we take advantage of the graphical structure among predictors to improve the performance of parameter estimation, model selection, and prediction in sparse quantile regression. It is shown under mild conditions that the proposed method enjoys the model selection consistency and the oracle properties. An alternating direction method of multipliers algorithm with a linearization technique is proposed to implement the proposed method numerically, and its convergence is justified. Simulation studies are conducted, showing that the proposed method is superior to existing methods in terms of estimation accuracy and predictive power. The proposed method is also applied to a real dataset
A priori and a posteriori · Algorithm · Data mining · Linearization · Machine learning · Mathematical optimization · Model selection · Nonlinear system · Oracle · Quantile · Quantile regression · Regression · Regression analysis · Statistics · Computer Science · Control Systems and Identification · Mathematics · Sparse and Compressive Sensing Techniques · Statistical Methods and Inference · Artificial Intelligence
Quantile Regression
Regularization and Variable Selection Via the Elastic Net
The Elements of Statistical Learning
The Adaptive Lasso and Its Oracle Properties
Gene set enrichment analysis
Sparse inverse covariance estimation with the graphical lasso
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
Regression Shrinkage and Selection Via the Lasso
Regression Quantiles
Robust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso
| Highly cited | No |
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