Sparsity identification in ultra-high dimensional quantile regression models with longitudinal data
Author:
Affiliation:
1. School of Statistics, Capital University of Economics and Business, Beijing, China;
2. Beijing Key Laboratory of Megaregions Sustainable Development Modelling, Beijing, China
Funder
National Social Science Foundation of China
Publisher
Informa UK Limited
Subject
Statistics and Probability
Link
https://www.tandfonline.com/doi/pdf/10.1080/03610926.2019.1604966
Reference35 articles.
1. ℓ1-penalized quantile regression in high-dimensional sparse models
2. Statistics for High-Dimensional Data
3. The Dantzig selector: Statistical estimation when p is much larger than n
4. Nonparametric independence screening and structure identification for ultra-high dimensional longitudinal data
5. Efficient estimation in semivarying coefficient models for longitudinal/clustered data
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4. Globally Adaptive Longitudinal Quantile Regression With High Dimensional Compositional Covariates;Statistica Sinica;2023
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