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Estimation of high dimensional mean regression in the absence of symmetry and light tail assumptions

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Cited by:

  1. Xin Wang & Lingchen Kong & Liqun Wang & Zhaoqilin Yang, 2023. "High-Dimensional Covariance Estimation via Constrained L q -Type Regularization," Mathematics, MDPI, vol. 11(4), pages 1-20, February.
  2. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
  3. Mengxi He & Xianfeng Hao & Yaojie Zhang & Fanyi Meng, 2021. "Forecasting stock return volatility using a robust regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1463-1478, December.
  4. Li, Kangqiang & Tang, Songqiao & Zhang, Lixin, 2022. "Robust parameter estimation of regression models under weakened moment assumptions," Statistics & Probability Letters, Elsevier, vol. 191(C).
  5. Ding, Wenliang & Shu, Lianjie & Gu, Xinhua, 2023. "A robust Glasso approach to portfolio selection in high dimensions," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 22-37.
  6. 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).
  7. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
  8. Xing, Li-Min & Zhang, Yue-Jun, 2022. "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, vol. 110(C).
  9. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
  10. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
  11. Sung Hoon Choi & Donggyu Kim, 2023. "Large Global Volatility Matrix Analysis Based on Observation Structural Information," Papers 2305.01464, arXiv.org, revised Feb 2024.
  12. Yongxin Liu & Peng Zeng & Lu Lin, 2021. "Degrees of freedom for regularized regression with Huber loss and linear constraints," Statistical Papers, Springer, vol. 62(5), pages 2383-2405, October.
  13. Fan, Jianqing & Ke, Yuan & Liao, Yuan, 2021. "Augmented factor models with applications to validating market risk factors and forecasting bond risk premia," Journal of Econometrics, Elsevier, vol. 222(1), pages 269-294.
  14. Xianfeng Hao & Yudong Wang, 2023. "Forecasting the stock risk premium: A new statistical constraint," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1805-1822, November.
  15. Beyhum, Jad, 2019. "Inference robust to outliers with L1‐norm penalization," TSE Working Papers 19-1032, Toulouse School of Economics (TSE).
  16. Qin, Shanshan & Wu, Yuehua, 2020. "General matching quantiles M-estimation," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).
  17. Chen, Huangyue & Kong, Lingchen & Shang, Pan & Pan, Shanshan, 2020. "Safe feature screening rules for the regularized Huber regression," Applied Mathematics and Computation, Elsevier, vol. 386(C).
  18. Han, Dongxiao & Huang, Jian & Lin, Yuanyuan & Shen, Guohao, 2022. "Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 230(2), pages 416-431.
  19. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
  20. Jad Beyhum, 2020. "Inference robust to outliers with L1‐norm penalization," Post-Print hal-03235868, HAL.
  21. Sung Hoon Choi & Donggyu Kim, 2022. "Large Volatility Matrix Analysis Using Global and National Factor Models," Papers 2208.12323, arXiv.org, revised Dec 2022.
  22. Fan, Jianqing & Wang, Weichen & Zhong, Yiqiao, 2019. "Robust covariance estimation for approximate factor models," Journal of Econometrics, Elsevier, vol. 208(1), pages 5-22.
  23. Wang, Yudong & Hao, Xianfeng & Wu, Chongfeng, 2021. "Forecasting stock returns: A time-dependent weighted least squares approach," Journal of Financial Markets, Elsevier, vol. 53(C).
  24. Umberto Amato & Anestis Antoniadis & Italia De Feis & Irene Gijbels, 2021. "Penalised robust estimators for sparse and high-dimensional linear models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 1-48, March.
  25. Khan, Faridoon & Muhammadullah, Sara & Sharif, Arshian & Lee, Chien-Chiang, 2024. "The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models," Energy Economics, Elsevier, vol. 130(C).
  26. Yang, Shuquan & Ling, Nengxiang, 2023. "Robust projected principal component analysis for large-dimensional semiparametric factor modeling," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
  27. Luo, Jiyu & Sun, Qiang & Zhou, Wen-Xin, 2022. "Distributed adaptive Huber regression," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
  28. Fan, Jianqing & Guo, Yongyi & Jiang, Bai, 2022. "Adaptive Huber regression on Markov-dependent data," Stochastic Processes and their Applications, Elsevier, vol. 150(C), pages 802-818.
  29. 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.
  30. Hao, Xianfeng & Zhao, Yuyang & Wang, Yudong, 2020. "Forecasting the real prices of crude oil using robust regression models with regularization constraints," Energy Economics, Elsevier, vol. 86(C).
  31. 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.
  32. Choi, Sung Hoon & Kim, Donggyu, 2023. "Large volatility matrix analysis using global and national factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1917-1933.
  33. Chen, Likai & Wang, Weining & Wu, Wei Biao, 2019. "Inference of Break-Points in High-Dimensional Time Series," IRTG 1792 Discussion Papers 2019-013, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  34. Ciuperca, Gabriela, 2021. "Variable selection in high-dimensional linear model with possibly asymmetric errors," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
  35. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Papers 1912.06307, arXiv.org, revised Feb 2021.
  36. Guillaume Lecué & Mathieu Lerasle, 2017. "Robust machine learning by median-of-means : theory and practice," Working Papers 2017-32, Center for Research in Economics and Statistics.
  37. 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.
  38. Lecué, Guillaume & Lerasle, Matthieu, 2019. "Learning from MOM’s principles: Le Cam’s approach," Stochastic Processes and their Applications, Elsevier, vol. 129(11), pages 4385-4410.
  39. Yue Wang & Baobin Wang & Chaoquan Peng & Xuefeng Li & Hong Yin, 2022. "Huber Regression Analysis with a Semi-Supervised Method," Mathematics, MDPI, vol. 10(20), pages 1-12, October.
  40. Wang, Yibo & Karunamuni, Rohana J., 2022. "High-dimensional robust regression with Lq-loss functions," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
  41. Xiao, Xuan & Xu, Xingbai & Zhong, Wei, 2023. "Huber estimation for the network autoregressive model," Statistics & Probability Letters, Elsevier, vol. 203(C).
  42. 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.
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