含蓄草2023隐藏路线-搜狗指然

个人简介

开设课程

本科生: 概率论基础、数理统计
研究生: 统计推断、生存数据分析

研究方向

生存分析、高维数据分析、网络数据分析

教育经历

2012/09 -2017/07: 中科院数学与系统科学研究院
2008/09-2012/07: 山东农业大学，本科

工作经历

2022/07-至今: 含蓄草2023隐藏路线，副教授
2017/07-2022/06: 含蓄草2023隐藏路线，讲师
2016/01-2016/03: 香港中文大学统计系，访问学者，香港

研究成果

[1] Lianqiang Qu, Liuquan Sun and Yanqing Sun. (2024). A mark-specific quantile regression model. Biometrika, 111, 255–272.
[2] Yuqing Du (#), Lianqiang Qu, Ting Yan and Yuan, Zhang. (2023) Time-varying 𝜷-model for dynamic directed networks. Scandinavian Journal of Statistics, 50, 1687–1715.
[3] Dongxiao Han, Jian Huang, Yuanyuan Lin, Lei Liu, Lianqiang Qu and Liuquan Sun. (2023). Robust Signal Recovery for High-Dimensional Linear Log-Contrast Models with Compositional Covariates, Journal of Business & Economic Statistics, 41, 957-967.
[4] Lianqiang Qu, Xiaoyu Wang and Liuquan Sun. (2022). Variable screening for varying coefficient models with ultrahigh-dimensional survival data. Computational Statistics and Data Analysis, 172, 107498.
[5] Lianqiang Qu, Meiling Hao and Liuquan Sun. (2022). Sparse composite quantile regression with ultra-high dimensional heterogeneous data. Statistica Sinica, 32, 459-475.
[6] Meiling Hao*, Lianqiang Qu*, Dehan Kong, Liuquan Sun and Hongtu Zhu. (2021). Optimal minimax variable selection for large-scale matrix linear regression model. Journal of Machine Learning Research, 22, 1-39 (Co-first authors). 
[7] Dongxiao Han*, Lianqiang Qu*, Liuquan Sun and Yanqing Sun. (2021) Variable selection for the mark-specific additive hazards model using the adaptive Lasso. Statistical Methods in Medical Research, 30, 2017-2031 (Co-first authors).
[8] Lianqiang Qu and Liuquan Sun. (2021).  A non-marginal variable screening method for varying coefficient Cox model. Statistics and Its Interface, 14, 197–209. 
[9] Dongxiao Han, Meiling Hao, Lianqiang Qu and Wei Xu, (2020). A novel model for the X-chromosome inactivation association on survival data. Statistical Methods in Medical Research, 1305-1314.
[10] Lianqiang Qu and Liuquan Sun. (2019). The Cox-Aalen model for recurrent event data with a dependent terminal event. Statistica Neerlandica, 73, 234–255.
[11] Lianqiang Qu, Xinyuan Song and Liuquan Sun, (2018). Identification of local sparsity and variable selection for additive hazard model with varying coefficients.  Computational Statistics & Data Analysis, 125, 119-135.
[12] Ting Yan, Lianqiang Qu, Zhaohai, Li and Ao Yuan, (2018). Conditional kernel density estimation for some incomplete data models. Electronic Journal of Statistics, 12, 1299–1329.
[13] Lianqiang Qu, Liuquan Sun and Xinyuan Song, (2018). A joint modeling approach for longitudinal data with informative observation times and a terminal event. Statistics in Biosciences: 10, 609–633.
[14] Lianqiang Qu, Liuquan Sun and Lei Liu, (2017). Joint modeling of recurrent event data with a dependent terminal event. Statistics and Its Interface: 10, 699–710.
[15] Hu Zhang, Qinglong Yang and Lianqiang Qu, (2016).  A class of transformation rate models for recurrent event data. Science China Mathematics: 59, 2227-2244.

研究项目

(1)  上海市纳税风险识别模型技术研究(横向)，2015年;

(2)  湖北省自然科学基金青年项目: 带终止时间的复发事件数据统计建模及其在医学中的应用（2018.1-2020.12，结题）

(3)  国家自然科学基金青年项目：带竞争风险的分位数回归分析 （2021.1-2023.12，在研）

附件

曲连强

English Version (英文版)