9号彩票

文字
缩放
增大字体
减小字体
曼尼托巴大学 王熙逵教授等:Bandit models and their applications等

([西财新闻] 发布于 :2018-07-08 )

光华讲坛——社会名流与企业家论坛第5059

 

主题Bandit models and their applications

主讲人曼尼托巴大学 王熙逵教授

主持人统计学院 林华珍教授

时间201879(星期一)下午3:00-5:00

地点西南财经大学柳林校弘远楼408会议室

主办单位统计研究中心  统计学院  科研处

 

主题Bandit models and their applications

主讲人曼尼托巴大学 王熙逵教授

时间下午3:00-4:00

主讲人简介:

王熙逵,现为曼尼托巴大学研究生院副院长,理学院统计学系教授,他目前是 Communications in Statistics的副主编,已公开发表期刊论文50篇。

其研究兴趣是:Biostatistics , Bandit Processes, Markov Decision Processes, Mathematical Finance, Dynamic Pricing and Revenue Management

具体详情请见其个人主页:http://home.cc.umanitoba.ca/~wangx1/

主要内容

Bandit processes are statistical decision models for optimal sequential selections from several populations. Some of the populations have unknown statistical distributions. We follow the Bayesian approach to depict the information gathering process. After introducing bandit models, we address their potential applications in mathematical finance and other fields. The talk is based on joint work with several graduate students.

 

主题二Quantile Decision Trees and Forest with its application for predicting the risk (Post-Traumatic Stress Disorder) PTSD after experienced an acute coronary syndrome

主讲人哥伦比亚大学 韦颖教授

时间下午4:00-5:00

主讲人简介

韦颖,现为哥伦比亚大学生物统计系教授,2004年毕业于伊利诺伊大学香槟分校并获得统计学博士学位,现为JASASCIENCE CHINA, Mathematics的副主编。已公开发表期刊论文69篇。参与主持科研项目40余项。其研究兴趣包括:Semi-parametric models; multivariate quantiles; longitudinal data analysis; measurement errors; missing data; robust statistics.

详情请见其个人主页:https://yingweistat.com/

主要内容

Classification and regression trees (CART) are a classic statistical learning method that efficiently partitions the sample space into mutually exclusive subspaces with the distinctive means of an outcome of interest. It is a powerful tool for efficient subgroup analysis and allows for complex associations and interactions to achieve high prediction accuracy and stability. Hence, they are appealing tools for precision health applications that deal with large amounts of data from EMRs, genomics, and mobile data and aim to provide a transparent decision mechanism. Although there is a vast literature on decision trees and random forests, most algorithms identify subspaces with distinctive outcome means. The most vulnerable or high-risk groups for certain diseases are often patients with extremely high (or low) biomarker and phenotype values. However, means-based partitioning may not be effective for identifying patients with extreme phenotype values. We propose a new regression tree framework based on quantile regression \cite{KoenkerBassett1978} that partitions the sample space and predicts the outcome of interest based on conditional quantiles of the outcome variable. We implemented and evaluated the performance of the conditional quantile trees/forests to predict the risk of developing PTSD after experiencing an acute coronary syndrome (ACS), using an observational cohort data from the REactions to Acute Care and Hospitalization (REACH) study\cite{onge2017depressive} at New York Presbyterian Hospital. The results show that the conditional quantile based trees/forest have better discrimination power to identify patients with severe PTSD symptoms, in comparison to the classical mean based CART.


☆该新闻已被浏览: 次★

打印本文】 【关闭窗口



友情链接:财神汇彩票  熊猫彩票  万彩会彩票  环球彩票  博乐彩票