上海大学统计与大数据分析专场 | 首届青年统计学家论坛

Posted 2021-04-27 狗熊会

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上海大学统计与大数据分析专场 | 首届青年统计学家论坛

会议简介

为促进我国统计学发展和青年统计学者的学术交流，经全国工业统计学教学研究会2017年第一次会长办公会提议，第九届理事会第一次会议审议通过，决定成立全国工业统计学教学研究会“青年统计学家协会”（以下简称“协会”）。

协会旨在促进青年统计学家的交流，定期举办青年统计学家年会论坛。协会成立大会暨青年统计学家年会论坛已定于2019年4月19日在上海财经大学举办，会议邀请了数位在学术界和业界的优秀青年统计学家作为主旨演讲嘉宾，同时也欢迎社会各界的统计学家报名演讲或参会。本次首届会议的主题为：青年统计学家与数据产业。

会议时间

2019年4月19日下午、2019年4月20日全天

会议地点

上海财经大学

上海大学统计与大数据分析专场

专场负责人

倪中新教授、博士生导师

上海大学金融信息研究中心主任

主讲嘉宾及报告简介

应时辉

【个人简介】上海大学教授、博士生导师。中国运筹学会医疗运作管理分会理事。分别于 2001 年和 2008 年在西安交通大学获得学士和博士学位，2012.1-2013.1 在美国北卡罗来纳大学教堂山分校从事博士后研究。主要从事医学数据分析与机器学习中的数学理论与方法。目前主持国家自然科学基金面上项目 1 项，上海市能力建设项目1项，主持完成国家自然科学基金青年基金、教育部博士点基金各1 项，参与完成 973 计划 1项，国家自然科学基金 2 项。近年来，在NeuroImage、IEEE Trans.等杂志以及权威国际会议 CVPR 上发表论文三十余篇。研究成果被引600余次，包括被熊有伦院士、Navab会士等专家在IEEE T-PAMI, SIAM JIS等国际顶刊中的论文所正面报导。受邀担任多个SCI期刊客座编辑和著名国际会议TPC委员，在多个国内外重要学术会议的邀请和分组报告。

【报告题目】Distance Metric Learning for Best Description of Data Distribution

【报告摘要】

In this talk, we will present a best description of data distribution for classification by distance metric learning method. We first expend all descriptions of data distributions under the viewpoint of different metric action. Then, the problem for finding the best description is transferred to an optimization problem on the space of all metrics, i.e. the manifold of all positive definite matrices. To solve this optimization model, we proposed an intrinsic and structure-preserving method. Further, the linear distance metric learning is extended to the nonlinear case by applying the kernel technique. Finally, several experiments on classification validate that our proposed framework significantly improves the classification with the same computational efficiency and hence offers a way for best description of data distribution.

上海大学统计与大数据分析专场 | 首届青年统计学家论坛

石昕

【个人简介】博士，曼彻斯特城市大学应用统计学Reader，哈萨克斯坦卡拉干达医科大学副校长，上海大学商业大数据与应用统计中心的兼职教授和主任。。2007年至2009年期间，他在英国谢菲尔德大学医学院循证医学方向做博士后研究。2015年被英国皇家统计学会授予特许统计师（CStat）。他的主要研究兴趣在于医疗保健服务生命历程建模的商业分析领域。目前研究集中在使用非临床和相关的大数据来预测个性化健康管理及其决策的临床结果，例如，癌症早期诊断，在英国，中国和巴西等投入使用。另外，也是几位顶级期刊编辑和副主编。石昕博士还获得了皇家统计学会和高等教育机构等奖学金。他还为英国文化协会，NIHR，UNESCO等拨款审查委员会的研究资助担任顾问。

【报告题目】Big Data for Health Management—Early Diagnosis, Intervention and Prevention

【报告摘要】

Health is the most important demand for humans. Long and healthy life is one of the primary research subjects in human health research. However, it is difficult to accurately access health status at a very early stage, with the aim of determining appropriate interventions to maintain good health and wellbeing. Therefore, it is essential to optimise human health management polices and assess the risk factors associated with health status. Human health management is the process and means for health risk factors monitoring, prognostics, intervention and control based on our knowledge on human health and prevention using non-clinical and clinical linkage data. Some symptoms that could indicate potential advanced disease or chronic disease can often be ignored or missed. This will lead to serious delay in clinical diagnosis and timely treatment intervention. Subsequently, it will increase the medical treatment costs as well as increasing the patient’s physical, mental and financial burden. Our study aims to develop a systematic approach which integrates statistical health big data modelling into optimal health management decision-making with mobile application. By developing statistical modelling method for health big data on early diagnosis, prevention and intervention, we are developing a multi stage delay-time model to investigate risk factors and predict heath status at an earlier stage of disease/illness progression using linked clinical and non-clinical data. In this talk, we will our recent research outcomes and discuss the challenges for the future study.

孙凯

【个人简介】博士，上海大学经济学院副教授，上海市青年东方学者。2011年获美国纽约州立大学宾汉姆顿分校经济学博士学位，师从Subal C. Kumbhakar（舒伯利‧昆伯卡）。2011-2015年全职以经济学讲师的职位任教于英国阿斯顿大学商学院，2015-2016年全职以经济学高级讲师的职位任教于英国索尔福德大学商学院。曾兼职担任美国世界银行短期项目顾问。他的研究兴趣包括：随机边界分析以及应用微观非参数计量。他的论文发表在了诸如：European Journal of Operational Research, Journal of Applied Econometrics, Economics Letters, Energy Economics, European Journal of Finance, Technological Forecasting and Social Change, Empirical Economics, Journal of Productivity Analysis, Journal of Regulatory Economics, Review of Development Economics和Economic Inquiry等杂志上。他编写了局部线性半参数平滑系数模型以及估计环境政策变量对于技术无效率边际效应的R代码。

【报告题目】Estimation of A Four Component Semiparametric Stochastic Production Frontier Model with Endogenous Regressors and Determinants of Inefficiency

【报告摘要】

This paper proposes a semiparametric stochastic production frontier model where the technology parameters are unknown smooth functions of environmental variables, and inputs are allowed to be endogenous. There are four components in the error term of this stochastic frontier model, where two of them are the noise components including the time-invariant and time-varying noises, and the other two of them are the inefficiency components including the time-invariant (i.e., persistent) and time-varying (i.e., transient) inefficiencies. The transient inefficiency is allowed to be a function of the environmental variables as well. We apply the proposed methodology to the Norwegian salmon production data, and analyze the estimated smooth coefficients (i.e., input elasticities), marginal effects of farm age, and persistent, transient, and overall technical efficiency scores.

王宁

【个人简介】上海大学管理学院副教授。2006年至2010年就读于东北大学，获计算机科学与技术学士学位；2010年至2014年就读于香港城市大学，获管理科学博士学位。2014年10月进入上海大学管理学院，任讲师；2018年3月晋升为副教授。主持国家自然科学基金青年项目，上海市浦江人才计划，上海市软科学重点项目，上海高校青年教师培养资助计划，参与国家自然科学基金重点项目1项，面上项目3项，青年项目1项，教育部人文社会科学基金青年项目2项。获上海市教学成果一等奖。现任上海大学管理学院信息管理系教工党支部书记。研究领域为物流调度、供应链优化、商业数据分析。

【报告题目】Textual Sentiment of Chinese Microblog Toward the Stock Market

【报告摘要】

In this research, text mining and statistical models are deployed to explore the relationship between the Shanghai Stock Exchange Composite Index (SSECI) and the collective emotions of individual investors. The emotions of individual investors are quantified by extracting and aggregating investor online posts that contain finance-related keywords. To identify a set of finance-related keywords, three years of blogs from a famous financial blog site are segmented by an automatic text segmentation method; meanwhile, in the literature of social media, people typically select keywords manually. Posts that discuss the keywords are extracted out of all types of topics from Sina Weibo, the largest microblog platform in China. Statistical results reveal the relationship between daily posts and daily opening prices with a one-day lag, which indicates the existence of information (news) propagation lag. This study contributes to the existing literature by demonstrating that the microblog sentiment level reports can be quantitatively incorporated as a proxy to provide valuable support to portfolio decision making.

参会者报名