个人简介

国际统计学会(ISI) Elected member、国际数理统计学会（IMS）会员、中国现场统计协会资源与环境统计分会理事

教育经历

2009.9—2014.6 上海财经大学 统计与管理学院 统计学博士

2012.9—2014.5 耶鲁大学 公共卫生学院 生物统计系研究生研究员

2005.9—2009.6 南京审计学院 数学与应用数学系 学士

研究方向

组学遗传学统计建模、高维大数据统计计算、生存分析

科研项目

国家自然科学青年基金“高维单调转移模型的变量选择及其在违约风险评估中的应用” （编号：71501089），2016-2018年，项目负责人。

个人主页：

https://xingjieshi.github.io/

发表文章

1. Shi X., Chai X., Yang Y., Cheng Q., Jiao Y., Chen H, Huang J., Yang C, Liu  J. (2020) A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies. Nucleic Acids Research. 48(19): e109. [SCI, Impact Factor: 11.5]

2. Cheng Q, Yang Y, Shi X, Yeung K., Yang C., Peng H., Liu J. (2020) MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy. NAR Genomics and Bioinformatics

3. Shi X., Ma S., Huang Y. (2020). Promoting Sign Consistency in the Cox Proportional Hazards CureModel Selection. Statistical Methods in Medical Research, 29(1): 15-28 [SCI]

4. Yang Y, Shi X, Jiao Y., Huang J., Chen M., Zhou X., Sun L., Lin X., Yang C., Liu J. (2020) CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies. Bioinformatics, 36(7): 2009-16[SCI]

5. Liao X., Chai X.,Shi X., Chen L., Liu J.(2020) The statistical practice of the GTEx Project: from single to multiple tissues. Quantitative Biology . [SCI]

6. 史兴杰，王赛旎，李扬. (2020). 高维数据的稳健二分类方法. 统计研究，37(9): 95-105

7. Shi X., Yang Y., Jiao Y., Cheng C, Yang C., Lin X., Liu J. (2019). VIMCO: variational inference for multiple correlated outcomes in genome-wide association studies. Bioinformatics, 35(19): 3693-3700. [SCI]

8. Wang S., Shi X., Wu M., Ma S. (2019) Horizontal and vertical integrative analysis methods for mental disorders omics data. Scientific Report, 9(1): 13430 [SCI]

9. 孙怡凡，吴梦云，史兴杰. (2019).高维大数据基因网络中的社区发现——以NC方法为例. 统计研究, 36(3): 124-128.

10. Shi X., Huang Y., Huang J., Ma S. (2018). A forward and backward stagewise algorithm for nonconvex loss functions and adaptive lasso. Computational Statistics and Data Analysis, 124, 235-251.

11. Chai H*., Shi X.*, Zhang Q, Zhao Q, Huang Y, Ma S. (2017). Analysis of cancer gene expression data with an assisted robust marker identification approach.Genetic Epidemiology, 41: 779– 789. [SCI]

12. Liu J., Yang C., Shi X., Li C., Huang J., Zhao H., Ma S. (2016). Analyzing Association Mapping in Pedigree‐Based GWAS Using a Penalized Multitrait Mixed Model. Genetic Epidemiology,40(5): 382-393. [SCI]

13. Jiang Y.*, Shi X.*, Zhao Q., Krauthammer M., Rothberg BE., Ma S.(2016). Integrated analysis ofmultidimensional omics data on cutaneous melanoma prognosis. Genomics, 107(6): 223-30.

14. Shi X., Zhao Q., Huang J., Xie Y., Ma S. (2015). Deciphering the association between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach. Bioinformatics, 31(24): 3977-3983. [SCI]

15. Shi X.*, Yi H*, Ma S. (2015). Measures for the degree of overlap of gene signatures and applications to TCGA. Briefings in Bioinformatics, 16(5): 735-744. [SCI]

16. Wu C., Shi X., Cui Y., Ma S. (2015). A penalized robust semiparametric approach for gene–environment interactions. Statistics in Medicine, 34(30): 4016-30. [SCI]

17. Zhao Q.*, Shi X.*, Xie Y., Huang J., Shia B., Ma S. (2015). Combining Multidimensional Genomic Measurements for Predicting Cancer Prognosis: Observations from TCGA. Briefing in Bioinformatics, 16(2): 291-303.[SCI]

18.Zhao Q., Shi X., Huang J., Liu J., Li Y., Ma S. (2015). Integrative analysis of "-omics" data using penalty functions.WIREs Computational Statistics, 7(1): 99-108.

19. Shi X., Liu J., Huang J.,  Zhou Y., Shia B., Ma S. (2014). Integrative Analysis of Cancer Prognosis Data with Contrasted Group Bridge Penalization. Genetic Epidemiology, 38(2): 141-151. [SCI]

20. Shi X., Liu J., Huang J., Zhou Y., Xie Y., Ma S. (2014). A Penalized Robust Method for Identifying Gene-Environment Interactions.Genetic Epidemiology, 38(3): 220-230. [SCI]

21. Shi X., Shen S., Liu J., Huang J., Zhou Y., Ma S.(2014). Similarity of Markers Identified from Cancer Gene Expression Studies: Observations from GEO. Briefing in Bioinformatics, 15(5): 671-684.[SCI]

22. 史兴杰，周勇. (2014).房地产泡沫检验的Switching AR模型. 系统工程理论与实践, 34(3): 676-682 .

23. 刘玉涛，史兴杰，周勇. (2013).左截断右删失数据下光滑分布函数估计效率. 数学学报, 56(5): 625-636.