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统计学系系列讲座之265-268期

 

统计学系系列讲座之265期  
 
 
时间:2017 年 6月21日(星期四)9:30-10:30
 
地点:史带楼502室
 
主持人:朱仲义 教授 复旦大学管理学院统计学系
 
主题:Cluster Analysis of Longitudinal Profiles with Subgroups
 
主讲人:Prof. Annie Qu   University of Illinois at Urbana-Champaign
 
个人简介:瞿培勇教授是伊利诺伊大学香槟分校(UIUC)统计咨询中心主任,复旦大学上海千人计划教授,复旦大学讲座教授。
 
摘要:In this talk, we cluster profiles of longitudinal data using a penalized regression method. The uniqueness of our approach is that we allow individual variation of longitudinal patterns for each subject. Specifically, we utilize a pairwise-grouping penalization on the parameters corresponding to the nonparametric B-spline models, and thereby identify clusters based on different patterns of the predicted longitudinal curves. One advantage of the proposed method is that there is no need to pre-specify the number of clusters; instead the number of clusters is selected automatically through a model selection criterion. Our method is also applicable for unbalanced data where subjects could have different time points of measurements. To implement the proposed method, we develop an alternating direction method of multipliers (ADMM) algorithm which has the desirable convergence property. In theory, we establish the consistency properties asymptotically. In addition, we show that our method outperforms the existing competitive approaches in our simulation studies and real data example. This is joint work with Xiaolu Zhu.
 

 

统计学系系列讲座之266

 

时间:2017 年 6月22日(星期四)10:30-11:30
 
地点:史带楼503室
 
主持人:朱仲义 教授 复旦大学管理学院统计学系
 
主题:Semi-parametric method for non-ignorable missing in longitudinal data using refreshment samples
 
主讲人: Lan Xue PhD. Ohio State University
 
个人简介:Dr. Lan Xue is Associate Professor in Ohio State University. She got her PhD in Michigan State University in 2005. Dr. Xue's priamry research interests include Nonparametric curve estimation, Polynomial spline smoothing, Model selection, Time series analysis and Clustered/longitudinal data analysis. 
 
摘要:Missing data is one of the major methodological problems in longitudinal studies. It not only reduces the sample size, but also can result in biased estimation and inference. It is crucial to correctly understand the missing mechanism and appropriately incorporate it into the estimation and inference procedures. Traditional methods, such as the complete case analysis and imputation methods, are designed to deal with missing data under unverifiable assumptions of MCAR and MAR. The purpose of this talk is to identify and estimate missing mechanism parameters under the non-ignorable missing assumption utilizing the refreshment sample. In particular, we propose a semi-parametric method to estimate the missing mechanism parameters by comparing the marginal density estimator using Hirano's two constraint equations (Hirano et al. 1998) along with additional information from the refreshment sample. Asymptotic properties of semi-parametric estimators are developed. Inference based on bootstrapping is proposed and verified through simulations.

 

统计学系系列讲座之267

 

时间:2017 年 7月6日(星期四)10:30-11:30
 
地点:史带楼205室
 
主持人:沈娟 博士 复旦大学管理学院统计学系
 
主题:Penalized Maximum Tangent Likelihood Estimation and Variable Selection
 
主讲人:俞燕教授  美国辛辛那提大学 
 
个人简介:俞燕教授现为美国辛辛那提大学工商管理学院约瑟夫∙斯特恩讲座教授,终身(正)教授,博士导师,并任美国华人教授与科学家协会理事。曾任辛辛那提大学校学术委员会委员,院学术委员会主任。现为美国统计协会(ASA),管理科学协会(INFORMS),金融协会(AFA)会员等。曾为贝尔实验室,朗讯科技,瑞士信贷第一波士顿, 5/3 银行,杜克能源等提供咨询。俞燕博士获得美国康奈尔大学(Cornell)博士, Texas A&M大学硕士,中国科技大学本科学位。俞燕博士的研究方向和兴趣主要包括金融研究,期权定价,非参数估计方法,和大数据挖掘等。曾任顶尖专业杂志Journal of the American Statistical Association, Statistica Sinica副主编。
 
摘要:We introduce a new class of mean regression estimators --- penalized maximum tangent likelihood estimation --- for high-dimensional regression estimation and variable selection. We first explain the motivations for the key ingredient, maximum tangent likelihood estimation (MTE), and establish its asymptotic properties. We further propose a penalized MTE for variable selection and show that it is $\sqrt{n}$-consistent, enjoys the oracle property. The proposed class of estimators consists penalized l2 distance, penalized exponential squared loss, penalized least trimmed square and penalized least square as special cases and can be regarded as a mixture of minimum Kullback-Leibler distance estimation and minimum l2 distance estimation. Furthermore, we consider the proposed class of estimators under the high-dimensional setting when the number of variables d can grow exponentially with the sample size n, and show that the entire class of estimators (including the aforementioned special cases) can achieve the optimal rate of convergence in the order of $\sqrt{\ln(d)/n}$. Finally, simulation studies and real data analysis demonstrate the advantages of the penalized MTE. 

 

统计学系系列讲座之268期

 

时间:2017 年 6月27日(星期二)14:00-15:00

地点:史带楼503室

主持人:黎德元 教授 复旦大学管理学院统计学系

主题:A Gentle Introduction to Machine Learning in Insurance

主讲人:Prof. Mario V. Wüthrich  ETH Zurich, Switzerland 

个人简介:Mario V. Wüthrich is Professor in the Department of Mathematics at ETH Zurich, Honorary Visiting Professor at City University London and Honorary Professor at University College London. He holds a PhD in Mathematics from ETH Zurich. From 2000 to 2005, he held an actuarial position at Winterthur Insurance and was responsible for claims reserving in non-life insurance, as well as developing and implementing the Swiss Solvency Test. He is fully qualified actuary of the Swiss Association of Actuaries (SAA), serves on the board of the Swiss Association of Actuaries, and is editor of ASTIN Bulletin. 
 
摘要:Statistical problems in insurance become increasingly more complex because of new technology and bigger data warehouses. Machine learning provides very powerful tools that manage to solve involved statistical problems. The aim of this presentation is to bridge the gap between the actuarial community and the machine learning community. We present actuarial problems and discuss machine learning methods.

 

2017-6-16

 

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