1. Composite quantile regression for a distributed system with non-randomly distributed data. (2025). Chen, Yewen ; Hao, Chenyan ; Jin, Jun.
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   RePEc:spr:stpapr:v:66:y:2025:i:1:d:10.1007_s00362-024-01625-2.

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2. Global debiased DC estimations for biased estimators via pro forma regression. (2023). Li, Feng ; Lin, LU.
   In: TEST: An Official Journal of the Spanish Society of Statistics and Operations Research.
   RePEc:spr:testjl:v:32:y:2023:i:2:d:10.1007_s11749-023-00850-5.

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3. Communication-efficient sparse composite quantile regression for distributed data. (2023). Wang, Lei ; Yang, Yaohong.
   In: Metrika: International Journal for Theoretical and Applied Statistics.
   RePEc:spr:metrik:v:86:y:2023:i:3:d:10.1007_s00184-022-00868-z.

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4. A lack-of-fit test for quantile regression process models. (2023). Wang, Caixing ; Liu, Qiaochu ; Feng, Xingdong.
   In: Statistics & Probability Letters.
   RePEc:eee:stapro:v:192:y:2023:i:c:s0167715222001936.

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5. Composite quantile regression analysis of survival data with missing cause-of-failure information and its application to breast cancer clinical trial. (2023). Wu, Chengxin ; Zou, Yuye.
   In: Computational Statistics & Data Analysis.
   RePEc:eee:csdana:v:182:y:2023:i:c:s0167947323000221.

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6. Single-index composite quantile regression for ultra-high-dimensional data. (2022). Sun, Mengxian ; Jiang, Rong.
   In: TEST: An Official Journal of the Spanish Society of Statistics and Operations Research.
   RePEc:spr:testjl:v:31:y:2022:i:2:d:10.1007_s11749-021-00785-9.

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7. Optimal subsampling for composite quantile regression in big data. (2022). Yuan, Xiaohui ; Li, Yong ; Dong, Xiaogang ; Liu, Tianqing.
   In: Statistical Papers.
   RePEc:spr:stpapr:v:63:y:2022:i:5:d:10.1007_s00362-022-01292-1.

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8. Optimal subsampling for composite quantile regression model in massive data. (2022). Wang, Lei ; Shao, Yujing.
   In: Statistical Papers.
   RePEc:spr:stpapr:v:63:y:2022:i:4:d:10.1007_s00362-021-01271-y.

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9. Multi-round smoothed composite quantile regression for distributed data. (2022). Wang, Lei ; Di, Fengrui.
   In: Annals of the Institute of Statistical Mathematics.
   RePEc:spr:aistmt:v:74:y:2022:i:5:d:10.1007_s10463-021-00816-0.

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10. Robust estimation and regression with parametric quantile functions. (2022). Frumento, Paolo ; Sottile, Gianluca.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:171:y:2022:i:c:s0167947322000512.

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14. Robust and efficient estimating equations for longitudinal data partial linear models and its applications. (2021). Sun, Xiaofei ; Hao, Mengjie ; Wang, Kangning.
    In: Statistical Papers.
    RePEc:spr:stpapr:v:62:y:2021:i:5:d:10.1007_s00362-020-01181-5.

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15. Bayesian joint-quantile regression. (2021). Guo, Jianhua ; He, Xuming ; Wang, Huixia Judy ; Hu, Yingying.
    In: Computational Statistics.
    RePEc:spr:compst:v:36:y:2021:i:3:d:10.1007_s00180-020-00998-w.

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16. Advanced algorithms for penalized quantile and composite quantile regression. (2021). Jiang, Bei ; Kong, Linglong ; Gao, Jueyu ; Pietrosanu, Matthew ; Niu, DI.
    In: Computational Statistics.
    RePEc:spr:compst:v:36:y:2021:i:1:d:10.1007_s00180-020-01010-1.

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17. Copula and composite quantile regression-based estimating equations for longitudinal data. (2021). Shan, Wen ; Wang, Kangning.
    In: Annals of the Institute of Statistical Mathematics.
    RePEc:spr:aistmt:v:73:y:2021:i:3:d:10.1007_s10463-020-00756-1.

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18. Robust communication-efficient distributed composite quantile regression and variable selection for massive data. (2021). Zhang, Benle ; Li, Shaomin ; Wang, Kangning.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:161:y:2021:i:c:s0167947321000967.

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19. Hypothesis testing of varying coefficients for regional quantiles. (2021). Park, Seyoung ; Lee, Eun Ryung.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:159:y:2021:i:c:s0167947321000384.

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20. Composite quantile regression estimation of linear error-in-variable models using instrumental variables. (2020). Yang, Yiping.
    In: Metrika: International Journal for Theoretical and Applied Statistics.
    RePEc:spr:metrik:v:83:y:2020:i:1:d:10.1007_s00184-019-00734-5.

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21. Robust estimation of a location parameter with the integrated Hogg function. (2020). Luati, Alessandra ; Catania, Leopoldo.
    In: Statistics & Probability Letters.
    RePEc:eee:stapro:v:164:y:2020:i:c:s0167715220301152.

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22. Inference of local regression in the presence of nuisance parameters. (2020). Xu, Ke-Li.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:218:y:2020:i:2:p:532-560.

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23. Bayesian bridge-randomized penalized quantile regression. (2020). Song, Xinyuan ; Tian, Yuzhu.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:144:y:2020:i:c:s0167947319302312.

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24. Rank-based shrinkage estimation for identification in semiparametric additive models. (2019). Lu, Fang ; Yang, HU.
    In: Statistical Papers.
    RePEc:spr:stpapr:v:60:y:2019:i:4:d:10.1007_s00362-017-0874-z.

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25. Quantile regression and variable selection for partially linear model with randomly truncated data. (2019). Fan, Guo-Liang ; Wang, Jiang-Feng ; Chen, Zhen-Long ; Xu, Hong-Xia.
    In: Statistical Papers.
    RePEc:spr:stpapr:v:60:y:2019:i:4:d:10.1007_s00362-016-0867-3.

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26. Weighted composite quantile regression for single index model with missing covariates at random. (2019). Peng, Changgen ; Yang, HU ; Liu, Huilan.
    In: Computational Statistics.
    RePEc:spr:compst:v:34:y:2019:i:4:d:10.1007_s00180-019-00886-y.

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27. Sparse wavelet estimation in quantile regression with multiple functional predictors. (2019). Jiang, Bei ; Mizera, Ivan ; Zhang, LI ; Yu, Dengdeng ; Kong, Linglong.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:136:y:2019:i:c:p:12-29.

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28. Computation and application of robust data-driven bandwidth selection for gradient function estimation. (2019). Sun, Qiankun ; Xie, Qichang.
    In: Applied Mathematics and Computation.
    RePEc:eee:apmaco:v:361:y:2019:i:c:p:274-293.

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29. Weighted quantile regression and testing for varying-coefficient models with randomly truncated data. (2018). Wang, Jiang-Feng ; Chen, Zhen-Long ; Fan, Guo-Liang ; Xu, Hong-Xia.
    In: AStA Advances in Statistical Analysis.
    RePEc:spr:alstar:v:102:y:2018:i:4:d:10.1007_s10182-018-0319-6.

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30. Econometric Modeling of Risk Measures: A Selective Review of the Recent Literature. (2018). Fang, Ying ; Cai, Zongwu ; Tian, Dingshi.
    In: WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS.
    RePEc:kan:wpaper:201807.

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31. Composite quantile regression for GARCH models using high-frequency data. (2018). Wang, Meng ; Chen, Zhao.
    In: Econometrics and Statistics.
    RePEc:eee:ecosta:v:7:y:2018:i:c:p:115-133.

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32. Pairwise distance-based tests for conditional symmetry. (2018). Niu, Cuizhen ; Zhu, Lixing ; Li, Yong ; Guo, XU.
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    RePEc:eee:csdana:v:128:y:2018:i:c:p:145-162.

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33. Robust variable selection in high-dimensional varying coefficient models based on weighted composite quantile regression. (2017). Lv, Jing ; Yang, HU ; Guo, Chaohui.
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    RePEc:spr:stpapr:v:58:y:2017:i:4:d:10.1007_s00362-015-0736-5.

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34. An effective method to reduce the computational complexity of composite quantile regression. (2017). Tian, Maozai ; Wu, Yanke.
    In: Computational Statistics.
    RePEc:spr:compst:v:32:y:2017:i:4:d:10.1007_s00180-017-0749-8.

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35. Composite change point estimation for bent line quantile regression. (2017). Zhu, Zhongyi ; Wang, Huixia Judy ; Zhang, Liwen.
    In: Annals of the Institute of Statistical Mathematics.
    RePEc:spr:aistmt:v:69:y:2017:i:1:d:10.1007_s10463-015-0538-5.

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36. Multiple quantile regression analysis of longitudinal data: Heteroscedasticity and efficient estimation. (2017). Cho, Hyunkeun ; Kim, Mi-Ok.
    In: Journal of Multivariate Analysis.
    RePEc:eee:jmvana:v:155:y:2017:i:c:p:334-343.

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37. Nonparametric conditional quantile estimation: A locally weighted quantile kernel approach. (2017). Racine, Jeffrey ; Li, Kevin.
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    RePEc:eee:econom:v:201:y:2017:i:1:p:72-94.

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38. Composite quantile regression for correlated data. (2017). Song, Xinyuan ; Zhao, Weihua ; Lian, Heng.
    In: Computational Statistics & Data Analysis.
    RePEc:eee:csdana:v:109:y:2017:i:c:p:15-33.

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39. Single-index composite quantile regression with heteroscedasticity and general error distributions. (2016). Zhou, Zhan-Gong ; Qian, Wei-Min ; Jiang, Rong.
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    RePEc:spr:stpapr:v:57:y:2016:i:1:p:185-203.

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40. Bayesian Analysis of Composite Quantile Regression. (2016). Alhamzawi, Rahim.
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    RePEc:spr:stabio:v:8:y:2016:i:2:d:10.1007_s12561-016-9158-8.

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41. Composite quantile regression for single-index models with asymmetric errors. (2016). Sun, Jing.
    In: Computational Statistics.
    RePEc:spr:compst:v:31:y:2016:i:1:d:10.1007_s00180-016-0645-7.

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42. Estimation of linear composite quantile regression using EM algorithm. (2016). Zhu, Qianqian ; Tian, Maozai .
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    RePEc:eee:stapro:v:117:y:2016:i:c:p:183-191.

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43. Weighted composite quantile regression for single-index models. (2016). Jiang, Rong ; Zhou, Zhan-Gong ; Qian, Wei-Min .
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    RePEc:eee:jmvana:v:148:y:2016:i:c:p:34-48.

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44. Local composite quantile regression smoothing for Harris recurrent Markov processes. (2016). Li, Degui.
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    RePEc:eee:econom:v:194:y:2016:i:1:p:44-56.

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45. A propensity score adjustment method for regression models with nonignorable missing covariates. (2016). Jiang, Depeng ; Tang, Niansheng ; Zhao, Puying.
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    RePEc:eee:csdana:v:94:y:2016:i:c:p:98-119.

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46. Composite Quantile Periodogram for Spectral Analysis. (2016). Lim, Yaeji ; Oh, Hee-Seok .
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    RePEc:bla:jtsera:v:37:y:2016:i:2:p:195-221.

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47. Sufficient dimension folding for a functional of conditional distribution of matrix- or array-valued objects. (2015). Xue, Yuan ; Yin, Xiangrong .
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    RePEc:taf:gnstxx:v:27:y:2015:i:2:p:253-269.

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48. Robust comparison of regression curves. (2015). Zhu, Lixing ; Feng, Long ; Zou, Changliang ; Wang, Zhaojun.
    In: TEST: An Official Journal of the Spanish Society of Statistics and Operations Research.
    RePEc:spr:testjl:v:24:y:2015:i:1:p:185-204.

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49. Quantile regression methods with varying-coefficient models for censored data. (2015). Xie, Shangyu ; Zhou, Yong ; Wan, Alan T. K., .
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    RePEc:eee:csdana:v:88:y:2015:i:c:p:154-172.

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50. HYPOTHESIS TESTING FOR ARCH MODELS: A MULTIPLE QUANTILE REGRESSIONS APPROACH. (2015). Kim, Seonjin.
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    RePEc:bla:jtsera:v:36:y:2015:i:1:p:26-38.

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51. Local composite quantile regression estimation of time-varying parameter vector for multidimensional time-inhomogeneous diffusion models. (2014). Wang, Ji-Xia ; Xiao, Qing-Xian .
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    RePEc:taf:japsta:v:41:y:2014:i:11:p:2437-2449.

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52. Local rank estimation and related test for varying-coefficient partially linear models. (2014). Sun, Jing ; Lin, LU.
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    RePEc:taf:gnstxx:v:26:y:2014:i:1:p:187-206.

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53. Composite support vector quantile regression estimation. (2014). Seok, Kyungha ; Shim, Jooyong ; Hwang, Changha.
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    RePEc:spr:compst:v:29:y:2014:i:6:p:1651-1665.

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54. Testing in linear composite quantile regression models. (2014). Qian, Wei-Min ; Jiang, Rong ; Li, Jing-Ru.
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    RePEc:spr:compst:v:29:y:2014:i:5:p:1381-1402.

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55. Asymptotic normality for a local composite quantile regression estimator of regression function with truncated data. (2013). Zhang, Hui-Zeng ; Ma, Wei-Min ; Wen, Li-Min ; Wang, Jiang-Feng .
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56. Two step composite quantile regression for single-index models. (2013). Jiang, Rong ; Zhou, Zhan-Gong ; Chen, Yong ; Qian, Wei-Min .
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57. Variable selection and coefficient estimation via composite quantile regression with randomly censored data. (2012). Jiang, Rong ; Zhou, Zhangong ; Qian, Weimin .
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    RePEc:eee:stapro:v:82:y:2012:i:2:p:308-317.

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58. Local Walsh-average regression for semiparametric varying-coefficient models. (2012). Zou, Changliang ; Wang, Zhaojun ; Shang, Suoping .
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59. Local Walsh-average regression. (2012). Zou, Changliang ; Wang, Zhaojun ; Feng, Long.
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    RePEc:eee:jmvana:v:106:y:2012:i:c:p:36-48.

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1. Composite expectile estimation in partial functional linear regression model. (2024). Song, Xinyuan ; Yu, Ping ; Du, Jiang.
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2. Factor Model of Mixtures. (2023). Uryasev, Stanislav ; Peng, Cheng.
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3. Group penalized quantile regression. (2022). Oualkacha, Karim ; Benedet, Andrea L ; Yang, YI ; Ouhourane, Mohamed .
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4. Scoring predictions at extreme quantiles. (2022). , Almut ; Jana, Kaushik ; Gandy, Axel.
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   RePEc:spr:alstar:v:106:y:2022:i:4:d:10.1007_s10182-021-00421-9.

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5. A Multilevel Simulation Optimization Approach for Quantile Functions. (2022). Haskell, William Benjamin ; Ng, Szu Hui ; Wang, Songhao.
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   RePEc:inm:orijoc:v:34:y:2022:i:1:p:569-585.

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6. Inference in functional linear quantile regression. (2022). Staicu, Ana-Maria ; Maity, Arnab ; Wang, Kehui ; Li, Meng.
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   RePEc:eee:jmvana:v:190:y:2022:i:c:s0047259x22000264.

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7. GMM quantile regression. (2022). Sanroman, Graciela ; Poirier, Alexandre ; Pinto, Cristine ; Galvao, Antonio F ; Firpo, Sergio.
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   RePEc:eee:econom:v:230:y:2022:i:2:p:432-452.

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8. Adaptive quantile regressions for massive datasets. (2021). Liu, Xin ; Chen, Wei-Wei ; Jiang, Rong.
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   RePEc:spr:stpapr:v:62:y:2021:i:4:d:10.1007_s00362-020-01170-8.

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9. Bayesian joint-quantile regression. (2021). Guo, Jianhua ; He, Xuming ; Wang, Huixia Judy ; Hu, Yingying.
   In: Computational Statistics.
   RePEc:spr:compst:v:36:y:2021:i:3:d:10.1007_s00180-020-00998-w.

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10. Robust estimation of a location parameter with the integrated Hogg function. (2020). Luati, Alessandra ; Catania, Leopoldo.
    In: Statistics & Probability Letters.
    RePEc:eee:stapro:v:164:y:2020:i:c:s0167715220301152.

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11. Quantile regression in varying-coefficient models: non-crossing quantile curves and heteroscedasticity. (2018). Verhasselt, A ; Gijbels, I ; Andriyana, Y.
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12. The dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk. (2018). GUPTA, RANGAN ; Caporin, Massimiliano ; Bonaccolto, G.
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13. Linear double autoregression. (2018). Zhu, Qianqian ; Li, Guodong ; Zheng, Yao.
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14. An effective method to reduce the computational complexity of composite quantile regression. (2017). Tian, Maozai ; Wu, Yanke.
    In: Computational Statistics.
    RePEc:spr:compst:v:32:y:2017:i:4:d:10.1007_s00180-017-0749-8.

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15. Composite change point estimation for bent line quantile regression. (2017). Zhu, Zhongyi ; Wang, Huixia Judy ; Zhang, Liwen.
    In: Annals of the Institute of Statistical Mathematics.
    RePEc:spr:aistmt:v:69:y:2017:i:1:d:10.1007_s10463-015-0538-5.

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16. Multiple quantile regression analysis of longitudinal data: Heteroscedasticity and efficient estimation. (2017). Cho, Hyunkeun ; Kim, Mi-Ok.
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    RePEc:eee:jmvana:v:155:y:2017:i:c:p:334-343.

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17. A COMPARATIVE STUDY OF MONOTONE QUANTILE REGRESSION METHODS FOR FINANCIAL RETURNS. (2016). Cai, Yuzhi.
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18. On Bayesian Quantile Regression Using a Pseudo-joint Asymmetric Laplace Likelihood. (2016). Ghosh, Pulak ; Ramamoorthi, R V ; Sriram, Karthik.
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19. The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective. (2016). Caporin, Massimiliano ; Bonaccolto, Giovanni.
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20. Analyzing Bank Efficiency: Are Too-Big-to-Fail Banks Efficient?. (2015). Sickles, Robin ; Liu, Junrong ; Jacobs, Michael ; Inanoglu, Hulusi .
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21. Variable selection via composite quantile regression with dependent errors. (2015). Song, Xinyuan ; Zhu, Zhongyi ; Tang, Yanlin.
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22. HYPOTHESIS TESTING FOR ARCH MODELS: A MULTIPLE QUANTILE REGRESSIONS APPROACH. (2015). Kim, Seonjin.
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24. Asymptotics for panel quantile regression models with individual effects. (2012). Montes-Rojas, Gabriel ; Galvao, Antonio F. ; Kato, Kengo.
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25. Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression. (2010). Kai, Bo ; Li, Runze ; Zou, Hui.
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