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2016年研究成果

1. Huazhen Lin*, Zhe Fei and Yi Li(2016). A semiparametrically efficient estimator of the time-varying effects for survival data with time-dependent treatment. Scandinavian Journal of Statistics, 43, 649-663.

2. He, K., Li, Y., Zhu, J., Liu, H., Lee, J., Amos, C., Hyslop, T., Jin, J., Lin, H.,Wei, Q. and Li, Y*. (2016). Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates. Bioinformatics, 32,50-57.

3. 蒋家坤, 林华珍*, 蒋靓, Paul S. F. YIP (2016). 门槛回归模型中门槛值和回归参数的估计。《中国科学》,46,409-422.

4. Huazhen Lin*, Ming T. Tan and Yi Li (2016). A Semiparametrically Efficient Estimator of Single-index Varying Coefficient Cox Proportional Hazards Models. Statistica Sinica, 26, 779-807.

5. Huazhen Lin*, Ye He, Jian Huang (2016). A global partial likelihood estimation in the additive Cox proportional hazards model. Journal of Statistical Planning and Inference, 169, 71-87.

6. Ling Zhou, Huazhen Lin and Yi-Chen Lin* (2016). Education, Intelligence, and Well-Being: Evidence from a Semiparametric Latent Variable Transformation Model for Multiple Outcomes of Mixed Types. Social Indicators Research, 125, 1011-1033.

7.Wei Lan, Yue Ding, Zheng Fang and Kuangnan Fang (2016),“TestingCovariates in High Dimension Linear Regression with Latent Factors”,Journal of Multivariate Analysis, 144,25—37.

8. 严成樑,李涛,兰伟(2016),“金融发展、创新与二氧化碳排放”,《金融研究》2016年第1期。

9. Wei Lan, Ping-Shou Zhong, Runze Li, Hansheng Wang and Chih-Ling Tsai (2016),“Testing a Single Regression Coefficient in High Dimensional Linear Models,”Journal of Econometrics, 195, 154--168.

10. Jing Zhou, On Kit Tam, and Wei Lan (2016), “Solving agency problems in Chinese family firms-A law and finance perspective,”Asian Business & Management, 15,57--82.

11. Jing Zhou, Wei Lan and Tang, Y (2016), “The value of institutional shareholders: Evidence from cross-border acquisitions by Chinese listed firms,”Management Decision, 54,44-65.

12. Yunlong Feng,Shao-Gao Lv*,Hanyuan Hang,Johan A. K. Suykens,(2016),Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery,Neural Computation ,28, 525–562 .

13. Shaogao Lv, Xin He, Junhui Wang(2016) A unified penalized method for sparse additive quantile models: an RKHS approach,Ann Inst Stat Math,DOI 10.1007/s10463-016-0566-9

14. Guo Niu, Zhengming Ma, Shaogao Lv(2016)Ensemble Multiple-Kernel Based Manifold Regularization,Neural Process Lett,DOI 10.1007/s11063-016-9543-9

17. Lei Yang、Shaogao Lv、Junhui Wang(2016) Model-free Variable Selection in Reproducing Kernel Hilbert Space,Journal of Machine Learning Research,17 ,1-24

15. Shaogao Lv and Luhong Wang (2016) Linearity Identification for General Partial Linear Single-Index Models ,Mathematical Problems in Engineering,Volume 2016, Article ID 3537564, 7 pages

16. JINYUAN CHANG,∗, QI-MAN SHAO, AND WEN-XIN ZHOU,(2016)CRAMÉR-TYPE MODERATE DEVIATIONS FOR STUDENTIZED TWO-SAMPLE U-STATISTICS WITH APPLICATIONS,The Annals of Statistics, Vol. 44, No. 5, 1931–1956

17. Chang, J., Tang, C. Y. & Wu, Y. (2016). Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood,The Annals of Statistics, 44, 515–539.

18. Zhang, S., Okhrin, O., Zhou, Q. M., and Peter, X.-K. S.(2016) Goodness-of-fit test for specification of semiparametric copula dependence models,Journal of Econometrics,193(1), 215-233

19.Xuerong Chen,∗, Niansheng Tang, Yong Zhou,(2016)Quantile regression of longitudinal data with informative observation times,Journal of Multivariate Analysis,144,176–188

20.XUERONG CHEN、YEQIAN LIU、JIANGUO SUN、YONG ZHOU(2016) Semiparametric Quantile Regression Analysis of Right-censored and Length-biased Failure Time Data with Partially Linear Varying Effects,Scandinavian Journal of Statistics,43: 921 –938

21.Shuangzhe Liu, Victor Leiva, Tiefeng Ma, Alan Welsh, 2016, Influence diagnostic analysis in the possibly heteroskedastic linear model with exact restrictions. Statistical Methods & Applications, 25 227-249.

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