论文专著
1.Xie, X., Geng, Z. and Zhao, Q. (2006) Decomposition of structural learning about directed acy...[全部]
1.Xie, X., Geng, Z. and Zhao, Q. (2006) Decomposition of structural learning about directed acyclic graphs. Artificial Intelligence, 170, 422-439.
2.Ma, Z. M., Xie, X. C. and Geng, Z. (2006) Collapsibility of distribution dependence. J. Royal Statist. Soc. Ser. B 68, 127-133.
3.Ju, C. and Geng, Z. (2010) Criteria for surrogate endpoints based on causal distributions. J. Royal Statist. Soc. B 72, 129-142.
4.Ding, P., Geng, Z., Yan, W. and Zhou, X. H. (2011) Identifiability and estimation of causal effects by principal stratification with outcomes truncated by death. J. Am. Statist. Asso., 106, 1578-1591.
5.Ma, Z., Xie, X. and Geng, Z. (2008) Structural learning of chain graphs via decomposition. J. Machine Learning Research, 9, 2847-2880.
6.Xie, X. and Geng, Z. (2008) A recursive method for structural learning of directed acyclic graphs. J. Machine Learning Research, 9, 459-483.
7.He, Y. and Geng, Z. (2008) Active learning of causal networks with intervention experiments and optimal designs. J. Machine Learning Research, 9, 2523-2547.
8.Chen, H., Geng, Z. and Jia, J. (2007) Criteria for surrogate end points. J. Royal Statist. Soc. Ser. B 69, 919-932.
9.Deng, K., Geng, Z. and Liu, J. (2014) Association pattern discovery via theme dictionary models. J. Royal Statist Soc. B 76, 319-347.
10.Deng, W., Geng, Z. and Li, H. (2013) Learning local directed acyclic graphs based on multivatriate time series data. Annals of Applied Statistics, 7, 1663-1683.
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研发成果
研究领域为数理统计学、生物医学统计、因果推断。在因果推断、因果网络、不完全数据分析等方面取得多项...[全部]
研究领域为数理统计学、生物医学统计、因果推断。在因果推断、因果网络、不完全数据分析等方面取得多项研究成果。提出Surrogate Paradox,论证因果作用的可识别性，研究非随机缺失数据的统计推断，提出因果网络的各种学习方法。成果发表在统计学(J Royal Statist Soc B, J Am Statist Asso, Ann Appl Statist)、生物医学统计(Biometrics, Biometrika, Statist Medicine)、机器学习(J Mach Learn Res)、人工智能(Artificial Intelligence)等刊物上。[收起]