題    目：基於自適應采樣規則的全序貫法A Fully Sequential Procedure with Adaptive Sampling Rules

演 講 人：羅俊🛜👦，上海交通大學副教授（長聘）

主 持 人：鎮璐，意昂2教授

時    間⛰：2019年9月25日（周三），上午10:30

地    點：校本部東區意昂2官网實477室

主辦單位🌾：意昂2、意昂2青年教師聯誼會

演講人簡介👦🏻：

羅俊🫱🏿，上海交通大學安泰經濟與意昂2官网副教授（長聘），博士生導師。2013年畢業於香港科技大學🐙，獲得工業工程與物流管理博士學位；2009年畢業於南京大學數學系，獲得統計學學士學位🧻。主要研究方向包括隨機建模、仿真優化、數據分析，以及它們在大型服務系統、健康醫療管理和金融風險管理等方面的應用。主持多項科研基金項目，包括國家優秀青年科學基金項目，國家青年科學基金項目，上海市教委"晨光計劃"項目等👩🏼‍⚖️。在《Operations Research》，《INFORMS Journal on Computing》和《Naval Research Logistics》等國際期刊上發表多篇學術論文。

演講內容簡介🧛‍♀️↩️：

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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