IDEAS home Printed from https://ideas.repec.org/r/eee/stapro/v2y1984i6p323-325.html
   My bibliography  Save this item

A note on L-estimates for linear models

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Kato, Kengo & F. Galvao, Antonio & Montes-Rojas, Gabriel V., 2012. "Asymptotics for panel quantile regression models with individual effects," Journal of Econometrics, Elsevier, vol. 170(1), pages 76-91.
  2. Inanoglu, Hulusi & Jacobs, Michael, Jr. & Liu, Junrong & Sickles, Robin, 2015. "Analyzing Bank Efficiency: Are "Too-Big-to-Fail" Banks Efficient?," Working Papers 15-016, Rice University, Department of Economics.
  3. Yanlin Tang & Xinyuan Song & Zhongyi Zhu, 2015. "Variable selection via composite quantile regression with dependent errors," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(1), pages 1-20, February.
  4. Yanke Wu & Maozai Tian, 2017. "An effective method to reduce the computational complexity of composite quantile regression," Computational Statistics, Springer, vol. 32(4), pages 1375-1393, December.
  5. Li, Meng & Wang, Kehui & Maity, Arnab & Staicu, Ana-Maria, 2022. "Inference in functional linear quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
  6. Yuzhi Cai, 2016. "A Comparative Study Of Monotone Quantile Regression Methods For Financial Returns," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 1-16, May.
  7. Bonaccolto, G. & Caporin, M. & Gupta, R., 2018. "The dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 446-469.
  8. 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.
  9. Yu, Ping & Song, Xinyuan & Du, Jiang, 2024. "Composite expectile estimation in partial functional linear regression model," Journal of Multivariate Analysis, Elsevier, vol. 203(C).
  10. Songhao Wang & Szu Hui Ng & William Benjamin Haskell, 2022. "A Multilevel Simulation Optimization Approach for Quantile Functions," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 569-585, January.
  11. Zhao, Quanshui, 2000. "Restricted Regression Quantiles," Journal of Multivariate Analysis, Elsevier, vol. 72(1), pages 78-99, January.
  12. Bo Kai & Runze Li & Hui Zou, 2010. "Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 49-69, January.
  13. Y. Andriyana & I. Gijbels & A. Verhasselt, 2018. "Quantile regression in varying-coefficient models: non-crossing quantile curves and heteroscedasticity," Statistical Papers, Springer, vol. 59(4), pages 1589-1621, December.
  14. Qi Zheng & Colin Gallagher & K.B. Kulasekera, 2013. "Adaptively weighted kernel regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(4), pages 855-872, December.
  15. Firpo, Sergio & Galvao, Antonio F. & Pinto, Cristine & Poirier, Alexandre & Sanroman, Graciela, 2022. "GMM quantile regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 432-452.
  16. Liwen Zhang & Huixia Judy Wang & Zhongyi Zhu, 2017. "Composite change point estimation for bent line quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 145-168, February.
  17. Yingying Hu & Huixia Judy Wang & Xuming He & Jianhua Guo, 2021. "Bayesian joint-quantile regression," Computational Statistics, Springer, vol. 36(3), pages 2033-2053, September.
  18. Cho, Hyunkeun & Kim, Seonjin & Kim, Mi-Ok, 2017. "Multiple quantile regression analysis of longitudinal data: Heteroscedasticity and efficient estimation," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 334-343.
  19. Rong Jiang & Wei-wei Chen & Xin Liu, 2021. "Adaptive quantile regressions for massive datasets," Statistical Papers, Springer, vol. 62(4), pages 1981-1995, August.
  20. Giovanni Bonaccolto & Massimiliano Caporin, 2016. "The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective," JRFM, MDPI, vol. 9(3), pages 1-25, July.
  21. Catania, Leopoldo & Luati, Alessandra, 2020. "Robust estimation of a location parameter with the integrated Hogg function," Statistics & Probability Letters, Elsevier, vol. 164(C).
  22. Cheng Peng & Stanislav Uryasev, 2023. "Factor Model of Mixtures," Papers 2301.13843, arXiv.org, revised Mar 2023.
  23. Axel Gandy & Kaushik Jana & Almut E. D. Veraart, 2022. "Scoring predictions at extreme quantiles," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(4), pages 527-544, December.
  24. Karthik Sriram & R. V. Ramamoorthi & Pulak Ghosh, 2016. "On Bayesian Quantile Regression Using a Pseudo-joint Asymmetric Laplace Likelihood," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 78(1), pages 87-104, February.
  25. Zhu, Qianqian & Zheng, Yao & Li, Guodong, 2018. "Linear double autoregression," Journal of Econometrics, Elsevier, vol. 207(1), pages 162-174.
  26. Seonjin Kim, 2015. "Hypothesis Testing For Arch Models: A Multiple Quantile Regressions Approach," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 26-38, January.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.