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Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension

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  1. Dongdong Zhang & Shaohua Pan & Shujun Bi & Defeng Sun, 2023. "Zero-norm regularized problems: equivalent surrogates, proximal MM method and statistical error bound," Computational Optimization and Applications, Springer, vol. 86(2), pages 627-667, November.
  2. Liu, Yan & Bai, Zhidong & Li, Hua & Hu, Jiang & Lv, Zhihui & Zheng, Shurong, 2022. "RDS free CLT for spiked eigenvalues of high-dimensional covariance matrices," Statistics & Probability Letters, Elsevier, vol. 187(C).
  3. Fan, Zengyan & Lian, Heng, 2018. "Quantile regression for additive coefficient models in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 164(C), pages 54-64.
  4. Sokbae Lee & Myung Hwan Seo & Youngki Shin, 2016. "The lasso for high dimensional regression with a possible change point," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 193-210, January.
  5. Xingcai Zhou & Zhaoyang Jing & Chao Huang, 2024. "Distributed Bootstrap Simultaneous Inference for High-Dimensional Quantile Regression," Mathematics, MDPI, vol. 12(5), pages 1-53, February.
  6. Ma, Haiqiang & Li, Ting & Zhu, Hongtu & Zhu, Zhongyi, 2019. "Quantile regression for functional partially linear model in ultra-high dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 135-147.
  7. Man, Rebeka & Tan, Kean Ming & Wang, Zian & Zhou, Wen-Xin, 2024. "Retire: Robust expectile regression in high dimensions," Journal of Econometrics, Elsevier, vol. 239(2).
  8. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
  9. Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2024. "Reprint: Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 239(2).
  10. Wang, Shangshan & Xiang, Liming, 2017. "Two-layer EM algorithm for ALD mixture regression models: A new solution to composite quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 136-154.
  11. Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2018. "Oracle Estimation of a Change Point in High-Dimensional Quantile Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1184-1194, July.
  12. Hao, Meiling & Lin, Yuanyuan & Shen, Guohao & Su, Wen, 2023. "Nonparametric inference on smoothed quantile regression process," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
  13. Weihua Zhao & Jianbo Li & Heng Lian, 2018. "Adaptive varying-coefficient linear quantile model: a profiled estimating equations approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(3), pages 553-582, June.
  14. Mohamed Ouhourane & Yi Yang & Andréa L. Benedet & Karim Oualkacha, 2022. "Group penalized quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 495-529, September.
  15. Haoze Hou & Wei Huang & Zheng Zhang, 2025. "Non-parametric Quantile Regression and Uniform Inference with Unknown Error Distribution," Papers 2504.01761, arXiv.org.
  16. De Gooijer, Jan G. & Zerom, Dawit, 2019. "Semiparametric quantile averaging in the presence of high-dimensional predictors," International Journal of Forecasting, Elsevier, vol. 35(3), pages 891-909.
  17. Xiang Zhang & Yichao Wu & Lan Wang & Runze Li, 2016. "Variable selection for support vector machines in moderately high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 53-76, January.
  18. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
  19. Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2023. "Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 235(1), pages 166-179.
  20. Chen, Le-Yu & Lee, Sokbae, 2023. "Sparse quantile regression," Journal of Econometrics, Elsevier, vol. 235(2), pages 2195-2217.
  21. Fan, Rui & Lee, Ji Hyung & Shin, Youngki, 2023. "Predictive quantile regression with mixed roots and increasing dimensions: The ALQR approach," Journal of Econometrics, Elsevier, vol. 237(2).
  22. Zhao, Jun & Chen, Yingyu & Zhang, Yi, 2018. "Expectile regression for analyzing heteroscedasticity in high dimension," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 304-311.
  23. Li, Xiang & Li, Yu-Ning & Zhang, Li-Xin & Zhao, Jun, 2024. "Inference for high-dimensional linear expectile regression with de-biasing method," Computational Statistics & Data Analysis, Elsevier, vol. 198(C).
  24. Haowen Bao & Zongwu Cai & Yuying Sun & Shouyang Wang, 2023. "Penalized Optimal Forecast Combination for Quantile Regressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202514, University of Kansas, Department of Economics, revised May 2025.
  25. Monika Bhattacharjee & Nilanjan Chakraborty & Hira L. Koul, 2023. "Weighted l1‐Penalized Corrected Quantile Regression for High‐Dimensional Temporally Dependent Measurement Errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(5-6), pages 442-473, September.
  26. Liqun Yu & Nan Lin, 2017. "ADMM for Penalized Quantile Regression in Big Data," International Statistical Review, International Statistical Institute, vol. 85(3), pages 494-518, December.
  27. Yao, Fang & Sue-Chee, Shivon & Wang, Fan, 2017. "Regularized partially functional quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 156(C), pages 39-56.
  28. Kaul, Abhishek & Koul, Hira L., 2015. "Weighted ℓ1-penalized corrected quantile regression for high dimensional measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 72-91.
  29. Eun Ryung Lee & Seyoung Park & Sang Kyu Lee & Hyokyoung G. Hong, 2023. "Quantile forward regression for high-dimensional survival data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 769-806, October.
  30. Xiaofeng Shao & Jingsi Zhang, 2014. "Martingale Difference Correlation and Its Use in High-Dimensional Variable Screening," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1302-1318, September.
  31. Jun Zhao & Guan’ao Yan & Yi Zhang, 2022. "Robust estimation and shrinkage in ultrahigh dimensional expectile regression with heavy tails and variance heterogeneity," Statistical Papers, Springer, vol. 63(1), pages 1-28, February.
  32. Lina Liao & Cheolwoo Park & Hosik Choi, 2019. "Penalized expectile regression: an alternative to penalized quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(2), pages 409-438, April.
  33. Lu, Wenqi & Qin, Guoyou & Zhu, Zhongyi & Tu, Dongsheng, 2021. "Multiply robust subgroup identification for longitudinal data with dropouts via median regression," Journal of Multivariate Analysis, Elsevier, vol. 181(C).
  34. Li, Lu & Ke, Chenlu & Yin, Xiangrong & Yu, Zhou, 2023. "Generalized martingale difference divergence: Detecting conditional mean independence with applications in variable screening," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
  35. Yuyang Liu & Pengfei Pi & Shan Luo, 2023. "A semi-parametric approach to feature selection in high-dimensional linear regression models," Computational Statistics, Springer, vol. 38(2), pages 979-1000, June.
  36. Chen, Xirong & Li, Degui & Li, Qi & Li, Zheng, 2019. "Nonparametric estimation of conditional quantile functions in the presence of irrelevant covariates," Journal of Econometrics, Elsevier, vol. 212(2), pages 433-450.
  37. Honda, Toshio & 本田, 敏雄 & Lin, Chien-Tong, 2022. "Forward variable selection for ultra-high dimensional quantile regression models," Discussion Papers 2021-02, Graduate School of Economics, Hitotsubashi University.
  38. Sherwood, Ben, 2016. "Variable selection for additive partial linear quantile regression with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 206-223.
  39. Miaomiao Wang & Xinyu Zhang & Alan T. K. Wan & Kang You & Guohua Zou, 2023. "Jackknife model averaging for high‐dimensional quantile regression," Biometrics, The International Biometric Society, vol. 79(1), pages 178-189, March.
  40. Luo, Bin & Gao, Xiaoli, 2022. "High-dimensional robust approximated M-estimators for mean regression with asymmetric data," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
  41. Mohamed Ouhourane & Karim Oualkacha & Archer Yi Yang, 2024. "Group penalized expectile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(5), pages 1251-1313, November.
  42. Demian Pouzo, 2015. "On the Non-Asymptotic Properties of Regularized M-estimators," Papers 1512.06290, arXiv.org, revised Oct 2016.
  43. Castiglione, Cristian & Arnone, Eleonora & Bernardi, Mauro & Farcomeni, Alessio & Sangalli, Laura M., 2025. "PDE-regularised spatial quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 205(C).
  44. Shaogao Lv & Xin He & Junhui Wang, 2017. "A unified penalized method for sparse additive quantile models: an RKHS approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(4), pages 897-923, August.
  45. Park, Seyoung & Kim, Hyunjin & Lee, Eun Ryung, 2023. "Regional quantile regression for multiple responses," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
  46. Alvaro Mendez-Civieta & M. Carmen Aguilera-Morillo & Rosa E. Lillo, 2021. "Adaptive sparse group LASSO in quantile regression," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 547-573, September.
  47. Zhang, Shucong & Zhou, Yong, 2018. "Variable screening for ultrahigh dimensional heterogeneous data via conditional quantile correlations," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 1-13.
  48. He, Qianchuan & Kong, Linglong & Wang, Yanhua & Wang, Sijian & Chan, Timothy A. & Holland, Eric, 2016. "Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 222-239.
  49. Kong, Yinfei & Li, Yujie & Zerom, Dawit, 2019. "Screening and selection for quantile regression using an alternative measure of variable importance," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 435-455.
  50. Wu, Xiaofei & Ming, Hao & Zhang, Zhimin & Cui, Zhenyu, 2024. "Multi-block alternating direction method of multipliers for ultrahigh dimensional quantile fused regression," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
  51. Xianchao Xiu & Lingchen Kong & Yan Li & Houduo Qi, 2018. "Iterative reweighted methods for $$\ell _1-\ell _p$$ ℓ 1 - ℓ p minimization," Computational Optimization and Applications, Springer, vol. 70(1), pages 201-219, May.
  52. Tang, Yanlin & Wang, Huixia Judy & Zhu, Zhongyi, 2013. "Variable selection in quantile varying coefficient models with longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 435-449.
  53. Kean Ming Tan & Lan Wang & Wen‐Xin Zhou, 2022. "High‐dimensional quantile regression: Convolution smoothing and concave regularization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 205-233, February.
  54. Lian, Heng & Meng, Jie & Fan, Zengyan, 2015. "Simultaneous estimation of linear conditional quantiles with penalized splines," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 1-21.
  55. Weirong Li & Wensheng Zhu, 2024. "Subgroup analysis with concave pairwise fusion penalty for ordinal response," Statistical Papers, Springer, vol. 65(6), pages 3327-3355, August.
  56. Zhaoping Hong & Yuao Hu & Heng Lian, 2013. "Variable selection for high-dimensional varying coefficient partially linear models via nonconcave penalty," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(7), pages 887-908, October.
  57. Xianwen Ding & Jiandong Chen & Xueping Chen, 2020. "Regularized quantile regression for ultrahigh-dimensional data with nonignorable missing responses," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(5), pages 545-568, July.
  58. Toshio Honda & Chien-Tong Lin, 2023. "Forward variable selection for ultra-high dimensional quantile regression models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(3), pages 393-424, June.
  59. He, Xuming & Pan, Xiaoou & Tan, Kean Ming & Zhou, Wen-Xin, 2023. "Smoothed quantile regression with large-scale inference," Journal of Econometrics, Elsevier, vol. 232(2), pages 367-388.
  60. Méndez Civieta, Álvaro & Aguilera Morillo, María del Carmen & Lillo Rodríguez, Rosa Elvira, 2019. "Quantile regression : a penalization approach," DES - Working Papers. Statistics and Econometrics. WS 28428, Universidad Carlos III de Madrid. Departamento de Estadística.
  61. Xia, Xiaochao & Liu, Zhi & Yang, Hu, 2016. "Regularized estimation for the least absolute relative error models with a diverging number of covariates," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 104-119.
  62. Wang, Yibo & Karunamuni, Rohana J., 2022. "High-dimensional robust regression with Lq-loss functions," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
  63. Eun Ryung Lee & Hohsuk Noh & Byeong U. Park, 2014. "Model Selection via Bayesian Information Criterion for Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 216-229, March.
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