学术交流
概率统计及其应用系列报告(三)

信息来源:暂无 发布日期: 2017-08-29浏览次数:


学术报告一

报告题目: Multiple two-sample tests: sparsity, FDR and power

: 刘卫东教授上海交通大学、国家优秀青年基金获得者、国家万人计划青年拔尖人才及教育部新世纪优秀人才

报告摘要:

Current algorithms for association rule mining from transaction data are mostly deterministic and enumerative. They can be computationally intractable even for mining a dataset containing just a few hundred transaction items, if no action is taken to constrain the search space. In this talk, we introduce a Gibbs-sampling-induced stochastic search procedure to randomly sample association rules from the itemset space, and perform rule mining from the reduced transaction dataset generated by the sample. A general rule importance measure is also proposed to direct the stochastic search so that, as a result of the randomly generated association rules constituting an ergodic Markov chain, the overall most important rules in the itemset space can be uncovered from the reduced dataset with probability 1 in the limit.  We end the talk by presenting simulation studies and real data examples.

报告时间: 2017年1013(周)  15:30-16:30

报告地点: 磬苑校区数学科学学院H306


学术报告二

报告题目: Krigings Over Space and Time Based on Latent Low-Dimensional Structures

: 张荣茂教授浙江大学,统计研究所副所长

报告摘要:

We propose a new approach to represent nonparametrically the linear dependence structure of a spatio-temporal process in terms of latent common factors. Though it is formally similar to the existing reduced rank approximation methods (Section 7.1.3 of Cressie and Wikle, 2011), the fundamental difference is that the low-dimensional structure is completely unknown in our setting, which is learned from the data collected irregularly over space but regularly over time. We do not impose any stationarity conditions over space either, as the learning is facilitated by the stationarity in time. Krigings over space and time are carried out based on the learned low-dimensional structure. Their performance is further improved by a newly proposed aggregation method via randomly partitioning the observations accordingly to their locations. A low-dimensional correlation structure also makes the krigings scalable to the cases when the data are taken over a large number of locations and/or over a long time period. Asymptotic properties of the proposed methods are established. Illustration with both simulated and real data sets is also reported. (A joint work with Professors Yao, Q. W. and Huang, D.)

报告时间: 2017年1013(周)  16:30-17:30

报告地点: 磬苑校区数学科学学院H306

欢迎各位老师、同学届时前往!

数学科学学院

         科学技术处

                        20171012

报告人简介: 

刘卫东,上海交通大学教授,国家优秀青年基金获得者,国家万人计划青年拔尖人才及教育部新世纪优秀人才,目前是Journal of Statistical Planning and Inference期刊Associate editor2008年于浙江大学获得博士学位,2010年获全国百篇优秀博士学位论文及由世界华人数学家大会颁发的新世界数学奖。2008-2009年在香港科技大学数学系从事博士后研究工作,2009-2011年在美国宾夕法尼亚大学沃顿商学院统计系从事博士后研究工作。研究兴趣包括高维统计推断、生物统计、非线性时间序列分析、非参数模型、新统计方法等,已发表50余篇高质量论文,其中包括国际概率统计顶级期刊Journal of the American Statistical Association、Annals of Statistics、Biometrika、JRSS(B)Annals of Applied ProbabilityProbability Theory and Related Fields。2013年被评为上海市浦江人才计划2014年被评为上海市曙光人才计划2015年被评为上海市东方学者特聘教授

张荣茂,浙江大学教授,现任统计研究所副所长、浙江省现场统计研究会副理事长、Journal of Korean Statistical Society (SCI)和International Journal of Mathematical Statistics的编委。主要从事非平稳时间序列和空间数据的理论与应用研究,研究兴趣包括大样本统计理论非线性金融时间序列经验似然估计非参数统计空间数据分析等。已在国际重要SSCI/SCI杂志发表论文40于篇,其中包括国际顶尖概率统计期刊Annals of StatisticsJournal of the American Statistical AssociationJournal of Econometrics。主持浙江省杰出青年基金、国家自然科学基金和教育部基金多项。