在国内外重要刊物上发表科研论文20余篇,代表性成果如下: 1.Guo Chaohui(郭朝会),Li, Jialiang*, 2020. Homogeneity and structure identification in semiparametric factor models. Journal of Business & Economic Statistics(计量经济学顶级期刊), in press, https://doi.org/10.1080/07350015.2020.1831516 (SCI). 2.Tu, Jingwen, Yang, Hu, Guo Chaohui*(郭朝会) Lv Jing, 2020. Model averaging marginal regression for high dimensional conditional quantile prediction. Statistical Papers, in press, https://doi.org/10.1007/s00362-020-01212-1 (SCI). 3.Lv Jing, Guo Chaohui*(郭朝会),Wu Jibo, 2019. Subject-wise empirical likelihood inference for robust joint mean-covariance model with longitudinal data. Statistics and Its Interface, 12: 617–630. (SCI) 4.Lv Jing, Guo Chaohui*(郭朝会), Wu Jibo, 2019. Smoothed empirical likelihood inference via the modified Cholesky decomposition for quantile varying coefficient models with longitudinal data. TEST, 28:999–1032. (SCI) 5. Lv Jing, Guo Chaohui*(郭朝会), 2019. Quantile estimations via modified Cholesky decomposition for longitudinal single-index models. Annals of the institute of statistical mathematics, 71:1163–1199. (SCI) 6. Lv Jing, Guo Chaohui*(郭朝会), Li Tingting,Hao Yuanyuan, Pan Xiaolin, 2018. Adaptive robust estimation in joint mean–covariance regression model for bivariate longitudinal data [J]. Statistics, 52:64-83. (SCI) 7.吕晶,郭朝会*,杨虎,李婷婷,2018. 纵向数据的有效秩推断基于修正的Cholesky分解. 数学学报中文版,61: 549-568. 8.Guo Chaohui*(郭朝会), Yang Hu, Lv Jing, 2018. Two step estimations for a single-index varying-coefficient model with longitudinal data. STATISTICAL PAPERS, 59: 957–983. 9.Lv Jing, Guo Chaohui*(郭朝会), Yang Hu, Li Yalian, 2017. A moving average Cholesky factor model in covariance modeling for composite quantile regression with longitudinal data [J]. Computational Statistics and Data Analysis, 112: 129-144. (SCI) 10. Lv Jing, Guo Chaohui*(郭朝会), 2017. Efficient parameter estimation via modified Cholesky decomposition for quantile regression with longitudinal data [J]. Computational Statistics, 32: 947-975. (SCI) 11.Guo Chaohui(郭朝会), Yang Hu, Lv Jing*, 2017. Robust variable selection in high-dimensional varying coefficient models based on weighted composite quantile regression. STATISTICAL PAPERS, 58(4): 1009-1033. (SCI) 12.Guo Chaohui(郭朝会), Yang Hu, Lv Jing*, 2017. Robust variable selection for generalized linear models with a diverging number of parameters. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 46(6): 2967-2981. (SCI) 13.Guo Chaohui(郭朝会), Yang Hu, Lv Jing*, Wu, Jibo, 2016. Joint estimation for single index mean-covariance models with longitudinal data. JOURNAL OF THE KOREAN STATISTICAL SOCIETY, 45(4): 526-543. (SCI) 14.Guo Chaohui*(郭朝会), Yang Hu, Lv Jing, 2016. Generalized varying index coefficient models. JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 300(1): 1-17. (SCI) 15. Yang Hu, Guo Chaohui *(郭朝会), Lv Jing, 2016. Variable selection for generalized varying coefficient models with longitudinal data. STATISTICAL PAPERS, 57:115–132. (SCI) 16.Yang Hu, Guo Chaohui *(郭朝会), Lv Jing, 2015.SCAD penalized rank regression with a diverging numberof parameters. Journal of Multivariate Analysis, 133: 321–333. (SCI) 17.Yang Hu, Guo Chaohui *(郭朝会), Lv Jing, 2014. A robust and efficient estimation method for single-index varying-coefficient models. Statistics and Probability Letters, 94 :119–127.(SCI) |