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2018概率统计及其应用系列报告之十三(李启寨、金百锁、王占锋)

  发布日期:2018-12-10  浏览量:472



报告题目I: 高维缺失数据的EM算法

报 告 人I: 李启寨(中国科学院数学与系统科学研究院研究员国家优秀青年科学基金获得者)

报告摘要:Missing data are frequently encountered in high dimensional problems, but they are usually difficult to deal with by using standard algorithms, such as the expectation–maximization algorithm and its variants. To tackle this difficulty, some problem-specific algorithms have been developed in the literature, but there still lacks a general algorithm.This work is to fill the gap:we propose a general algorithm for high dimensional missing data problems. The algorithm works by iterating between an imputation step and a regularized optimization step. At the imputation step, the missing data are imputed conditionally on the observed data and the current estimatesof parameters and, at the regularized optimization step, a consistent estimate is found via the regularization approach for the minimizer of a Kullback–Leibler divergence defined on the pseudocomplete data. For high dimensional problems, the consistent estimate can be found under sparsity constraints. The consistency of the averaged estimate for the true parameter can be established under quite general conditions. The algorithm is illustrated by using high dimensional Gaussian graphical models, high dimensional variable selection and a random coefficient model.

报告时间: 20181213(周四)15:30-16:15

报告地点:磬苑校区澳门赌搏网站大全H306

报告题目II: Simultaneous Multiple Change-point Detection in the Spatio-Temporal Linear Models

报 告 人II: 金百锁中国科学技术大学管理学院副教授

报告摘要:We consider a general class of spatio-temporal linear models, where the number of predictors can tend to infinity at each time point. A procedure for simultaneously detecting multiple change-points is developed rigorously via the construction of adaptive group lasso penalty. Consistency of the multiple change-point estimation is established under mild conditions even when the true number of change-points diverges with the sample size, i.e., the number of time points n. The adaptive group lasso can be substituted by the group lasso, and other non-convex group selection algorithms including group SCAD, and group MCP, etc. The simulation studies show that our procedure is accurate.  The housing transaction price in Baton Rouge and the commodity apartment price rises in Hong Kong are analyzed by the propose methodology.

报告时间: 20181213(周四) 16:15-17:00

报告地点:磬苑校区澳门赌搏网站大全H306

报告题目III: A robust t-process regression model with independent errors

报 告 人III: 王占锋中国科学技术大学管理学院副教授

报告摘要:Gaussian process regression (GPR) model is well-known to be susceptible to outliers. Robust process regression models based on t-process or other heavy-tailed processes have been developed to address the problem. However, due to the nature of the current definition for heavy-tailed processes, the unknown process regression function and the random errors are always defined jointly and thus dependently. This definition, mainly owing to thedependence assumption involved, is not justified in many practical problems and thus limits the application of those robust approaches. It also results in a limitation of the statistical properties and robust analysis. In this paper, we propose a new robust process regression model enabling independent random errors. An efficient estimation procedure is developed. We illustratethat the estimated random-effects are useful in detecting outlying curves.  Statistical properties, such as unbiasedness and information consistency, are provided. Numerical studies show that the proposed method is robust against outliers and has a better performance in prediction compared with the existing models.

报告时间: 20181213(周四) 17:00-17:45

报告地点:磬苑校区澳门赌搏网站大全H306


欢迎各位老师、同学届时前往!

数学科学学院

2018年12月10日

专家概况:

李启寨中国科学院数学与系统科学研究院研究员国家优秀青年基金获得者博士生导师2001年本科毕业于中国科学技术大学,2006年博士毕业于中国科学院数学与系统科学研究院2006—2009年在美国国家卫生健康研究院国家癌症研究所从事博士后研究;自2006—至今在中国科学院数学与系统科学研究院工资2006-2010年任助理研究员,2010-2015年任副研究员,2015—至今任研究员。主要从事生物医学统计、统计遗传学、农业统计分组检测、高维统计推断等方面的研究。截止目前在自然遗传学(Nature Genetice)、美国人类遗传学(American Journal of Human Genetics)、生物信息学(Bioninformatics)、美国统计学会会刊(Journal of the American Statistical Association)等杂志发表论文80余篇担任Scientific ReportsPLoSOneJournalof Applied StatisticsJournal of Systems Science & Complexity等杂志编委。曾获国际统计研究所推选会员、中国科学院卢嘉锡青年人才奖、中国工业与应用数学学会优秀青年学者奖、美国国家癌症研究所Fellow突出科研成果奖等。

金百锁,中国科学技术大学管理学院副教授。2001本科毕业于中国科学技术大学留校任教。在承担教学工作的同时于2006年取得理学博士学位导师缪柏其教授。博士毕业后在台湾中山大学从事为期一年的博士后研究。先后访问新加坡国立大学新加坡南洋理工大学香港浸会大学和加拿大约克大学。研究方向:大维随机矩阵实验设计模型选择变点检测、空间统计等等Annals of StatisticsAnnals of Applied ProbabiltyBiometrika等杂志发表论文34篇。主持国家自然科学基金青年项目一项,国家自然科学基金面上项目两项。

王占锋,中国科学技术大学统计与金融系副教授。分别于2003年和2008年获中国科学技术大学学士和理学博士学位2008-2010年台湾中央研究院统计研究所从事博士后工作。主要从事统计渐近理论、生物统计、函数型数据分析等领域的研究,在国内外学术期刊上发表论文30余篇。曾主持国家自然科学青年基金一项,参与国家重点自然科学基金一项和面上基金一项。2014年获得安徽省高等学校省级教坛新秀称号。


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