R package QICD. Estimate the Coefficients for Non-Convex Penalized Quantile Regression Model by using QICD Algorithm. Extremely fast algorithm ”QICD”, Iterative Coordinate Descent Algorithm for High-dimensional Nonconvex Penalized Quantile Regression. This algorithm combines the coordinate descent algorithm in the inner iteration with the majorization minimization step in the outside step. For each inner univariate minimization problem, we only need to compute a one-dimensional weighted median, which ensures fast computation. Tuning parameter selection is based on two different method: the cross validation and BIC for quantile regression model. Details are described in Peng,B and Wang,L. (2015) <<a href=”http://dx.doi.org/10.1080/10618600.2014.913516”>doi:10.1080/10618600.2014.913516</a>>.

References in zbMATH (referenced in 20 articles )

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  1. Tang, Yanlin; Wang, Yinfeng; Wang, Huixia Judy; Pan, Qing: Conditional marginal test for high dimensional quantile regression (2022)
  2. Wu, Xiaofei; Liang, Rongmei; Yang, Hu: Penalized and constrained LAD estimation in fixed and high dimension (2022)
  3. Zhang, Dongdong; Pan, Shaohua; Bi, Shujun: A proximal dual semismooth Newton method for zero-norm penalized quantile regression estimator (2022)
  4. Bonaccolto, Giovanni: Quantile-based portfolios: post-model-selection estimation with alternative specifications (2021)
  5. Fan, Ye; Lin, Nan; Yin, Xianjun: Penalized quantile regression for distributed big data using the slack variable representation (2021)
  6. Su, Meihong; Wang, Wenjian: Elastic net penalized quantile regression model (2021)
  7. Wang, Yanxin; Zhu, Li: Coordinate majorization descent algorithm for nonconvex penalized regression (2021)
  8. Xu, Q. F.; Ding, X. H.; Jiang, C. X.; Yu, K. M.; Shi, L.: An elastic-net penalized expectile regression with applications (2021)
  9. Ding, Xianwen; Chen, Jiandong; Chen, Xueping: Regularized quantile regression for ultrahigh-dimensional data with nonignorable missing responses (2020)
  10. Liu, Yongxin; Zeng, Peng; Lin, Lu: Generalized (\ell_1)-penalized quantile regression with linear constraints (2020)
  11. Rejchel, Wojciech; Bogdan, Małgorzata: Rank-based Lasso -- efficient methods for high-dimensional robust model selection (2020)
  12. Wang, Lan; Peng, Bo; Bradic, Jelena; Li, Runze; Wu, Yunan: A tuning-free robust and efficient approach to high-dimensional regression (2020)
  13. Alkenani, Ali; Msallam, Basim Shlaibah: Group identification and variable selection in quantile regression (2019)
  14. Jhong, Jae-Hwan; Koo, Ja-Yong: Simultaneous estimation of quantile regression functions using B-splines and total variation penalty (2019)
  15. Ma, Haiqiang; Li, Ting; Zhu, Hongtu; Zhu, Zhongyi: Quantile regression for functional partially linear model in ultra-high dimensions (2019)
  16. Shi, Yue Yong; Jiao, Yu Ling; Cao, Yong Xiu; Liu, Yan Yan: An alternating direction method of multipliers for MCP-penalized regression with high-dimensional data (2018)
  17. Shi, Yueyong; Wu, Yuanshan; Xu, Deyi; Jiao, Yuling: An ADMM with continuation algorithm for non-convex SICA-penalized regression in high dimensions (2018)
  18. Wang, Yanxin; Fan, Qibin; Zhu, Li: Variable selection and estimation using a continuous approximation to the (L_0) penalty (2018)
  19. Wu, Cen; Zhang, Qingzhao; Jiang, Yu; Ma, Shuangge: Robust network-based analysis of the associations between (epi)genetic measurements (2018)
  20. Mkhadri, Abdallah; Ouhourane, Mohamed; Oualkacha, Karim: A coordinate descent algorithm for computing penalized smooth quantile regression (2017)