学而不已 | 经济与管理学部一周学术讲座概览（12月13日-12月19日）
2021-12-12 14:17 浏览量: 2565
讲座总览
一、2021年12月13日（周一）1.叶志盛：Asymptotic Analysis of Data-Driven Multi-Stage Inventory Policies二、2021年12月15日（周三）1.王法硕：扎根理论的思想及在公共管理研究中的运用三、2021年12月16日（周四）1.Ying Jiao ：Dynamic Bivariate Mortality Modelling四、2021年12月17日（周五）1.罗珊：A Portmanteau Local Feature Discrimination Approach to the Classification with High-dimensional Matrix-variate Data2.卫然：Non-directed polymers in random environment in dimension d≥1详细讲座信息
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时间：2021年12月13日（周一）11：00-12：00地点：腾讯会议：966 255 549题目：Asymptotic Analysis of Data-Driven Multi-Stage Inventory Policies报告人：叶志盛 新加坡国立大学副教授主持人：唐炎林 研究员主办：统计学院摘要：We study periodic review stochastic inventory control in the data-driven setting, in which the retailer makes ordering decisions based only on historical demand observations without any knowledge of the probability distribution of the demand. Since an (s, S)-policy is optimal when the demand distribution is known, we investigate the statistical properties of the data-driven (s, S)-policy obtained by recursively computing the empirical cost-to-go functions (called DP-based estimator). This estimator is inherently challenging to analyze because the recursion induces propagation of the estimation error backward in time. In this work, we establish the asymptotic properties of this data-driven policy by fully accounting for the error propagation. First, we rigorously show the consistency of the estimated parameters by filling in some gaps (due to unaccounted error propagation) in the existing studies. On the other hand, empirical process theory cannot be directly applied to show asymptotic normality since the empirical cost-to-go functions for the estimated parameters are not i.i.d. sums, again due to the error propagation. Our main methodological innovation comes from an asymptotic representation for multi-sample U-processes in terms of i.i.d. sums. This representation enables us to apply empirical process theory to derive the influence functions of the estimated parameters and establish joint asymptotic normality. Based on these results, we also propose an entirely data-driven estimator of the optimal expected cost and we derive its asymptotic distribution. Beyond deriving the asymptotic distribution of our DP-based estimators, we further investigate the semiparametric efficiency of the proposed estimators. We show that the asymptotic variances of DP-based estimators match the statistical lower bound and so the proposed estimators are asymptotically efficient. The extensions to dependent demand are also investigated in this work, where we propose an SAA type estimator to estimate the optimal expected cost under base stock policies. We demonstrate some useful applications of our asymptotic results, including sample size determination, as well as interval estimation and hypothesis testing on vital parameters of the inventory problem. The results from our numerical simulations conform to our theoretical analysis.报告人简介：叶志盛，本科毕业于清华大学材料科学与工程系，博士就读于新加坡国大工业与系统工程系。现在为新加坡国大工业系统工程与管理系副教授。他的主要研究方向包括剩余寿命预测，可靠性建模，及数据驱动的运营决策。2
时间:2021年12月15日(周三)13：30-15：00地点:理科大楼A302室主题：扎根理论的思想及在公共管理研究中的运用主讲人：王法硕 华东师范大学副教授主持人:张冉 教授主办:公共管理学院摘要：扎根理论是一种质性研究方式，其主要宗旨是从经验资料出发建立理论。研究者直接从实际观察入手，从原始资料中归纳出经验概括，形成概念和范畴，然后上升到理论。扎根理论对于新现象与新问题具有较强的理论建构优势，近年来广泛运用于社会科学研究中。主讲人将针对扎根理论的主要思想、方法及其在公共管理部分热点主题中的应用进行汇报，旨在推进扎根理论在学院师生研究中的应用。报告人简介:王法硕，华东师范大学公共管理学院副教授、硕士生导师。华东师范大学社会组织与社会治理创新研究中心研究员，研究方向为数字治理与公共政策过程。主持省部级以上课题4项，发表核心期刊论文20余篇。3
时间：2021年12月16日（周四）19：00-20：00地点：腾讯会议，ID：163 429 294题目：Dynamic Bivariate Mortality Modelling报告人：Ying Jiao professor,University of Lyon 1主持人：钱林义 教授主办：统计学院摘要：We introduce a new approach to analyze the mortality dependence between two individuals in a couple which is intended to describe in a dynamic framework the joint mortality of married couples. The proposed framework aims to incorporate bivariate status information and capture, by adjusting some parametric form, the desired effect such as the “broken-heart syndrome”. To this end, we use a well-suited multiplicative decomposition, which will serve as a building block for the framework to relate the dependence structure and the marginals, and we make the link with existing practice of affine mortality models. Finally, we propose some illustrative examples and show how the underlying model allows to describe the main stylized facts of bivariate mortality dynamics. This is a joint work with Yahia Salhi and Shihua Wang.报告人简介：Ying Jiao is professor at University of Lyon 1 (Institut de Science Financière et d'Assurances, France). She has obtained a PhD in Applied Mathematics at Ecole Polytechnique and has worked previously at Paris VII University and Peking University. Her research interests are in the theory of stochastic processes and their applications in mathematical finance and risk modelling.4
时间：2021年12月17日（周五）10：00-11：00地点：理科大楼A302室题目：A Portmanteau Local Feature Discrimination Approach to the Classification with High-dimensional Matrix-variate Data主讲人：罗珊上海交通大学副教授主持人：王小舟 助理教授主办：统计学院摘要：Matrix-variate data arise in many scientific fields such as face recognition, medical imaging, etc. Matrix data contain important structure information which can be ruined by vectorization. Methods incorporating the structure information into analysis have significant advantages over vectorization approaches. In this article, we consider the problem of two-class classification with high-dimensional matrix-variate data, and propose a novel portmanteaulocal-feature discrimination (PLFD) method. This method first identifies local discrimination features of the matrix variate and then pools them together to construct a discrimination rule. We investigated the theoretical properties of the PLFD method and established its asymptotic optimality. We carried out extensive numerical studies including simulation and real data analysis to compare this method with other methods available in the literature, which demonstrate that the PLFD method has a great advantage over the other methods in terms of misclassification rate.报告人简介：罗珊，新加坡国立大学统计学博士，密歇根大学生物统计系访问学者。现为上海交通大学数学科学学院长聘副教授。主要研究领域为高维向量和矩阵数据、函数型数据、时空数据中的分类问题、模型选择标准和变量选择方法。文章主要发表在Journal of the American Statistical Association，Statistica Sinica，Journal of Multivariate Analysis，Sankhya A, Annals of the Institute of Statistical Mathematics，Computational Statistics and Data Analysis, Journal of Statistical Planning and Inference等期刊上。
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