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时 间：2017年06月26日（星期一）
地 点：西南财经大学柳林校区弘远楼402B会议室
主讲人一：中国科学院数学与系统科学研究院 王启华教授
时间： 上午10:00-11:00
主讲人简介：
王启华，中国科学院数学与系统科学研究院研究员（中科院核心骨干研究员），博士生导师，国家杰出青年基金获得者，教育部长江学者奖励计划特聘教授，中科院“百人计划”入选者, 国际统计学会当选会员. 1997 至今先后访问加拿大Carleton 大学、California 大学戴维斯分校、California 大学洛杉矶分校、美国Yale 大学、美国华盛顿大学、美国西北大学、德国Humboldt 大学、澳大利亚国立大学及澳大利亚悉尼大学等十多所国际知名大学。主要从事生存分析、缺失数据分析、高维数据统计分析及非- 半参数统计推断等领域的研究。共出版专著3部，Springer 出版社出版的书中2 章, 发表论文100 多篇， 其中90 多篇发表在JASA,The Annals of Statistics与Biometrika 等国际重要刊物，90 多篇被SCI 收录，2014、2015 及2016年连续3年被Elsevier 列入中国的专家榜单。
详情请见：https://baike.so.com/doc/5990286-6203253.html
主题：
LPRE criterion based estimating equation approaches for the error-in-covariables multiplicative regression models
摘要：
In this paper, we propose two estimating equation based methods to estimate the regression parameter vector in the multiplicative regression model when a subset of covariates are subject to measurement error but replicate measurements of their surrogates are available. Both methods allow the number of replicate measurements to vary between subjects. No parametric assumption is imposed on the measurement error term and the true covariates which are not observed in the data set. Under some regularity conditions, the asymptotic normality is established for both methods. Furthermore, our two proposed error corrected methods are compared in theoretical aspect when the distribution of the measurement error follows the normal distribution. Some simulation studies are conducted to assess the performances of the proposed methods. Real data analysis is used to illustrate our methods.
主讲人二：The Lundquist College of Business 吴文波教授
时间： 上午11:00-12:00
主讲人简介：
Dr. Wenbo Wu joined the Department of Operations and Business Analytics at the Lundquist College of Business in Fall 2015. He received his PhD in statistics from the University of Georgia. Professor Wu's primary research interests include dimension reduction, variable selection, multivariate analysis, data visualization, and data mining. He is also interested in developing statistical tools to solve practical problems in business
主 题：Learning heterogeneity in causal inference using sufficient dimension reduction
内容提要：
Often the research interest in causal inference is to see how the covariates affect the mean difference in the potential outcomes. In this paper, we use sufficient dimension reduction to estimate a lower dimensional linear combination of the covariates that are sufficient for this purpose. To enhance interpretability of the results, we further modify the estimator using sparse sufficient dimension reduction, which selects an active set of covariates for variable selection as a by-product. The estimator can also be used to test the heterogeneity of the causal effect. Compared to the existing methods, our approach is model-free, and avoids separate regression modeling in different treatment groups. Thus it can be more applicable and effective. These advantages are supported by both simulation studies and a real data example.
主讲人三：美国乔治亚州立大学 赵亦川教授
时间： 下午2:00-3:00
主讲人简介：
Dr. Yichuan Zhao is a Full Professor of Statistics, Georgia State University. Dr. Zhao has a B.S. and an M.S. in Mathematics from Peking University, and an M.S. in Stochastics and Operations Research from Utrecht University. He received his Ph.D. in Statistics at Florida State University. He has published 70 research articles in Statistics and Biostatistics research fields. Dr. Zhao has initiated Workshop Series on Biostatistics and Bioinformatics since 2012. He organized the 25th ICSA Applied Statistics Symposium in Atlanta as a chair of organizing committee and program committee with a great success. He is currently serving on editor or editorial board for several statistical journals, and served as a member of program committee in the numerous international conference. Dr. Zhao was an elected member of the International Statistical Institute.
题目：Empirical likelihood inference for the bivariate survival function with univariate censoring data
内容提要：
The empirical likelihood method is developed for constructing confidence intervals for the bivariate survival function in the presence of univariate censoring. It is shown that the empirical log-likelihood ratio has an asymptotic standard chi-squared distribution with one degree of freedom. Extensive simulation study shows that the proposed methods outperform the traditional method in finite samples. A real data set is analyzed for illustration of the proposed procedures.
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发表于 2017-11-6 10:05:47
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