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西安大略大学 何文清教授等:Perturbation-Based Tests with Application to the Clayton Model等

([西财新闻] 发布于 :2018-07-10 )

光华讲坛——社会名流与企业家论坛第5067

 

主题Perturbation-Based Tests with Application to the Clayton Model

主讲人西安大略大学 何文清教授等

主持人:统计学院 林华珍教授

时间2018712日(星期四)下午2:00-5:20

地点西南财经大学柳林校区弘远楼408会议室

主办单位:统计研究中心  统计学院  科研处

 

主题一Perturbation-Based Tests with Application to the Clayton Model

主讲人:西安大略大学 何文清教授

时间:下午2:00-2:45

主讲人简介

何文清,现为西安大略大学统计与精算科学学院教授,2002年从滑铁卢大学取得统计学哲学博士学位。已公开发表文章70余篇,主持科研项目21项。

现为Canadian Journal of Statistics Journal of Statistical Distributions and Applications Journal of Biometrics & Biostatistics Austin Statistics的副主编。

详情请见其个人主页:http://uwo.ca/stats/people/bios/wenqing-he.html

主要内容

Perturbation resampling method can be employed to estimate the covariance matrix of an estimator when the estimator is obtained through minimizing a U-process. This perturbation resampling is proposed to establish general tests for the detection of model misspecification or for model checking. The proposed tests enjoy simplicity and a theoretical justification. We apply the proposed method to modify the tests proposed by Shih (1998) for the assessment of Clayton models in multivariate survival analysis, where the asymptotic variance is intractable. The proposed tests present promising performance in the simulation studies and have simpler procedures than the nonparametric bootstrap which can also be applied to approximate the covariance matrix. A colon cancer study further illustrates the proposed methods.

 

主题二Causal Inference with Measurement Error in Outcomes

主讲人:滑铁卢大学 易耘教授

时间:下午2:45-3:30

主讲人简介

Grace Y. Yi is Professor of Statistics and University Research Chair at the University of Waterloo. Grace received her Ph.D. in Statistics from the University of Toronto in 2000. She is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. She is the Editor-in-Chief of The Canadian Journal of Statistics (2016-2018). She was President of the Biostatistics Section of The Statistical Society of Canada in 2016, and the Founder and Chair of the rst chapter (Canada Chapter) of The International Chinese Statistical Association. Grace’s research interests focus on developing statistical methodology to address various challenges arising from medical studies, clinical trials, epidemiology ,etc. Recently, her research monograph, “Yi, G. Y. (2017). Statistical Analysis with Measurement Error or Misclassication: Strategy, Method and Application”, has been published by Springer. Grace has published a broad range of academic papers in reputable journals, including Journal of The American Statistical Society, Biometrika, Journal of the Royal Statistical Society (Series B), Biometrics, Statistica Sinica, etc.Grace was the 2010 winner of the CRM-SSC Prize, an honor awarded in recognition of a statistical scientist’s professional accomplishments in research during the rst 15 years after having received a doctorate. She was a recipient of the prestigious University Faculty Award granted by the Natural Sciences and Engineering Research Council of Canada (NSERC). Her work with Xianming Tan and Runze Li won The Canadian Journal of Statistics Award in 2016. Grace has supervised many trainees at different levels, including post-doctoral fellows and undergraduate internship students. Two of her Ph.D. students (2015 and 2009) received The Pierre Robillard Award, a prestigious award in recognition of the best PhD thesis in the areas of probability and statistics defended in Canada each year (the award was established in 1978). Grace has served as an Associate Editor for statistical journals, including The Canadian Journal of Statistics, Journal of the Royal Statistical Society (Series C), The Journal of Applied Probability, Statistics in Biosciencs, STAT, and Biostatistics and Epidemiology. She has served on various professional committees and organizations, including the Mathematics and Statistics Evaluation Group of Discovery Grants Program at NSERC, the Fisher Lecture Committee of the American Statistical Association (ASA), the Development Committee for Canadian Statistical Science Institute (CANSSI), the Advisory Board of the Eastern North American Region (ENAR), and the Committee of the Distinguished Lecture in Statistical Sciences at the Fields Institute.

详情请见其个人主页:https://uwaterloo.ca/statistics-and-actuarial-science/people-profiles/grace-y-yi

主要内容

Inverse probability weighting (IPW) estimation has been popularly used to consistently estimate the average treatment effect (ATE). Its validity, however, is challenged by the presence of error-prone variables. In application, measurement error is ubiquitously present in data collection due to various reasons. Naively ignoring measurement error effects usually yields biased inference results.  In this talk, I will discuss the IPW estimation with mismeasured outcome variables.  The impact of measurement error for both continuous and discrete outcome variables will be examined. I will describe estimation procedures with the outcome misclassification effects accommodated. Consistency and efficiency will be investigated. 

Numerical studies will be reported to assess the performance of the proposed methods. 

 

主题三Disentangling and Assessing Uncertainties in Multiperiod Corporate Default Risk Prediction

主讲人:天普大学 汤琤咏副教授

时间:下午3:50-4:35

主讲人简介

汤琤咏,现为天普大学统计科学系副教授,2008年在爱荷华州立大学取得统计学博士学位,在他博士期间,他的Major Professor Dr. Song Xi Chen。他也是天普大学统计学系Graduate Programs的主任。他的研究兴趣包括:统计方法,高维数据分析,经验似然,纵向数据分析,金融统计与计量经济学,缺失数据的抽样统计与分析,非参数和半参数统计方法等。已公开发表期刊论文29篇。主持科研项目6项。他是International Statistical Institute (ISI)Elected MemberThe Royal Statistical SocietyFellow;他是ASA IMSICSAMember

详情请见其个人主页:https://sites.temple.edu/yongtang/

主要内容

Measuring credit risks for individual companies, industrial segments, and market systems is fundamentally and broadly important in economics, nance and beyond. For such a purpose, various quantitative methods have been developed to predictively assess the probabilities of companies going default in future. However, as a more difficult yet crucial problem, evaluating the uncertainties associated with the default predictions remains little explored. In this paper, we develop, for the rst time in the scenario of default predictions, a procedure for quantifying the level of associated uncertainties by carefully disentangling multiple contributing sources. Our framework effectively incorporates broad information from historical default data, nancial records, and economic environmental conditions by a) characterizing the default mechanism, and b) capturing the future dynamics of various features contributing to the default mechanism. Our development of the framework overcomes major challenges in this tremendously large scale statistical inference problem and makes it practically feasible by using parsimonious models, innovative methods, and modern computational facilities. By appropriately predicting the market-wise total number of defaults and assessing the associated uncertainties, our method can effectively evaluate the aggregated market credit risk level. Upon analyzing a US market data set with our method, we demonstrate that the level of uncertainties associated with default risk assessments is indeed substantial. More importantly and informatively, we also nd that the level of uncertainties associated with the default risk predictions is correlated with the level of default risks, indicating potential for beneting practical applications including improving the accuracy of default risk assessments. This is a joint work with Miao Yuan, Yili Hong, and Jian Yang.

 

主题四Two-sample functional linear models

主讲人:乔治华盛顿大学 梁华教授

时间:下午4:35-5:20

主讲人简介

梁华--乔治华盛顿大学统计系教授, 曾任美国罗切斯特大学医学院教授。出版英文学术著作2部,发表学术论文 150 多篇,其中24篇在 Annals of StatisticsBiometrikaJASA,和 JRSSB。他主持()8项美国国家科学基金会(NSF)以及美国国立卫生研究院(NIH)的研究项目。还主持1项海外-港澳学者研究基金(原杰出青年基金B, 2013-2019)

他是美国统计学会(ASA)、国际数理统计学会(IMS)和英国皇家统计学会(RSS)的fellow,国际统计学会(ISI)推举委员,也是JASA 等刊物的编委或副主编。

具体详情请见其个人主页:https://statistics.columbian.gwu.edu/hua-liang

主要内容

We study two-sample functional linear regression with a scaling transformation of regression functions. We consider estimation of the intercept, the slope function and the scalar parameter based on the functional principal component analysis. We also establish the rates of convergence for the estimator of the slope function, which is shown to be optimal in a minimax sense under certain smoothness assumptions. We further investigate semiparametric efficiency for the estimation of the scalar parameter and hypothesis testing. We also extend the proposed method to sparsely and irregularly sampled functional data and establish the consistency for the estimators of the scalar and the slope function. We evaluate numerical performance of the proposed methods through simulation studies and illustrate their utility via analysis of an AIDS data set.

 


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