學而不已 | 經(jīng)濟與管理學部一周學術(shù)講座概覽（12月13日-12月19日）
2021-12-12 14:17 瀏覽量: 3112
?智能總結(jié)講座總覽 一、2021年12月13日（周一）1.葉志盛：Asymptotic Analysis of Data-Driven Multi-Stage Inventory Policies二、2021...
講座總覽
一、2021年12月13日（周一）1.葉志盛：Asymptotic Analysis of Data-Driven Multi-Stage Inventory Policies二、2021年12月15日（周三）1.王法碩：扎根理論的思想及在公共管理研究中的運用三、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.羅珊：A Portmanteau Local Feature Discrimination Approach to the Classification with High-dimensional Matrix-variate Data2.衛(wèi)然：Non-directed polymers in random environment in dimension d≥1詳細講座信息
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時間：2021年12月13日（周一）11：00-12：00地點：騰訊會議：966 255 549題目：Asymptotic Analysis of Data-Driven Multi-Stage Inventory Policies報告人：葉志盛 新加坡國立大學副教授主持人：唐炎林 研究員主辦：統(tǒng)計學院摘要：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.報告人簡介：葉志盛，本科畢業(yè)于清華大學材料科學與工程系，博士就讀于新加坡國大工業(yè)與系統(tǒng)工程系。現(xiàn)在為新加坡國大工業(yè)系統(tǒng)工程與管理系副教授。他的主要研究方向包括剩余壽命預(yù)測，可靠性建模，及數(shù)據(jù)驅(qū)動的運營決策。2
時間:2021年12月15日(周三)13：30-15：00地點:理科大樓A302室主題：扎根理論的思想及在公共管理研究中的運用主講人：王法碩 華東師范大學副教授主持人:張冉 教授主辦:公共管理學院摘要：扎根理論是一種質(zhì)性研究方式，其主要宗旨是從經(jīng)驗資料出發(fā)建立理論。研究者直接從實際觀察入手，從原始資料中歸納出經(jīng)驗概括，形成概念和范疇，然后上升到理論。扎根理論對于新現(xiàn)象與新問題具有較強的理論建構(gòu)優(yōu)勢，近年來廣泛運用于社會科學研究中。主講人將針對扎根理論的主要思想、方法及其在公共管理部分熱點主題中的應(yīng)用進行匯報，旨在推進扎根理論在學院師生研究中的應(yīng)用。報告人簡介:王法碩，華東師范大學公共管理學院副教授、碩士生導師。華東師范大學社會組織與社會治理創(chuàng)新研究中心研究員，研究方向為數(shù)字治理與公共政策過程。主持省部級以上課題4項，發(fā)表核心期刊論文20余篇。3
時間：2021年12月16日（周四）19：00-20：00地點：騰訊會議，ID：163 429 294題目：Dynamic Bivariate Mortality Modelling報告人：Ying Jiao professor,University of Lyon 1主持人：錢林義 教授主辦：統(tǒng)計學院摘要：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.報告人簡介：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
時間：2021年12月17日（周五）10：00-11：00地點：理科大樓A302室題目：A Portmanteau Local Feature Discrimination Approach to the Classification with High-dimensional Matrix-variate Data主講人：羅珊上海交通大學副教授主持人：王小舟 助理教授主辦：統(tǒng)計學院摘要：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.報告人簡介：羅珊，新加坡國立大學統(tǒng)計學博士，密歇根大學生物統(tǒng)計系訪問學者?，F(xiàn)為上海交通大學數(shù)學科學學院長聘副教授。主要研究領(lǐng)域為高維向量和矩陣數(shù)據(jù)、函數(shù)型數(shù)據(jù)、時空數(shù)據(jù)中的分類問題、模型選擇標準和變量選擇方法。文章主要發(fā)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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