Jingjing Wu | Inferences and classification in two-component mixture models with stochastic dominance

时间:2018-08-26浏览:61设置

报告时间:8291600-1700

报告地点:闵行法商南楼135会议室

报告题目:Inferences and classification in two-component mixture models with stochastic dominance

报告人:Jingjing Wu University of Calgary

Abstract: In this work, we investigated a two-component nonparametric mixture model with stochastic dominance, a model arises naturally from genetic studies. Our interest lies in both the estimation of mixing proportion and classification. For this model, we first studied the model identifiability. Secondly, we proposed and studies two nonparametric estimation. Thirdly, in order to incorporate the stochastic dominance constraint, we introduced a semiparametric model for which we proposed and investigated a MLE and minimum Hellinger distance estimation (MHDE). Fourthly, we also proposed a hypothesis testing to test the validity of the semiparametric model. For the proposed methods, we investigated both their asymptotic properties such as consistency and asymptotic normality and their finite-sample performance through simulation studies and real data analysis.

报告人简介:吴静静,加拿大卡尔加里大学数学与统计系终身教授。1999年本科毕业于中央民族大学应用数学与软件专业,2002年硕士毕业于北京师范大学概率论与数理统计专业,2008年于加拿大艾伯塔大学获得统计学博士学位。博士论文被加拿大统计协会评为2007年度加拿大最佳概率统计博士论文奖。新加坡国立大学长期访问教授, 多个统计杂志的编委或/和审稿人,培养硕士生博士生20余名。主要研究方向包括半参数模型,基于最小距离的统计推断,大数据中的参数降维,及其在基因数据,生物统计,经济学等中的应用。发表学术论文20余篇,科研工作受加拿大自然科学与工程学委员会个人研究基金资助(2008-2023年)。



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