题    目：基于自适应采样规则的全序贯法A Fully Sequential Procedure with Adaptive Sampling Rules

演 讲 人：罗俊，上海交通大学副教授（长聘）

主 持 人：镇璐，太阳成集团tyc33455cc教授

时    间：2019年9月25日（周三），上午10:30

地    点：校本部东区太阳成集团tyc33455cc实477室

主办单位：太阳成集团tyc33455cc、太阳成集团tyc33455cc青年教师联谊会

演讲人简介：

罗俊，上海交通大学安泰经济与太阳成集团tyc33455cc副教授（长聘），博士生导师。2013年毕业于香港科技大学，获得工业工程与物流管理博士学位；2009年毕业于南京大学数学系，获得统计学学士学位。主要研究方向包括随机建模、仿真优化、数据分析，以及它们在大型服务系统、健康医疗管理和金融风险管理等方面的应用。主持多项科研基金项目，包括国家优秀青年科学基金项目，国家青年科学基金项目，上海市教委"晨光计划"项目等。在《Operations Research》，《INFORMS Journal on Computing》和《Naval Research Logistics》等国际期刊上发表多篇学术论文。

演讲内容简介：

Selecting the best system design from a finite set of alternatives is known as ranking-and-selection (R&S) in the simulation literature. Many procedures, from either frequentist or Bayesian approaches, has been designed in order to solve R&S problems more effectively or efficiently. Typically, frequentist procedures emphasize more on the effectiveness of a statistical guarantee while Bayesian procedures focus more on the efficiency of using a small number of total samples. In this paper, we aim to take both the effectiveness and efficiency into consideration, from the frequentist point of view. In particular, we design a fully sequential procedure with an adaptive sampling rule, which provide a probabilistic guarantee of correct selection in an asymptotic sense. We demonstrated both the effectiveness and efficiency of our proposed procedure by comparing with KN and OCBA, two classical procedures in frequentist and Bayesian frameworks, through extensive numerical experiments.

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