麻省理工学院(Massachusetts Institute of Technology)

创立于1861年, 坐落于美国马萨诸塞州剑桥市(大波士顿地区),是世界著名私立研究型大学。 作为世界顶尖高校,麻省理工学院尤其以自然及工程学享誉世界,位列2015-16 年世界大学学术排名(ARWU)工程学世界第1、计算机科学第2,与斯坦福大 学、加州大学伯克利分校一同被称为工程科技界的学术领袖。截至2017年,著名马萨诸塞州理工师生、校友或研究人员包括了91位诺贝尔奖得主、52位国家科学奖章获奖者、45位罗德学者、38名麦克阿瑟奖得主、6名菲尔兹奖获奖者、25位图灵奖得主。此校同时具很强的创业文化,由其校友所创办的公司利润总值相当于全球第十一大经济体。

麻省理工学院-数学统计科研

一、课题方向

Statistics, Modeling, and Computation

统计、建模和计算

二、科研内容参考

This summer research program at MIT focuses on Mathematical Statistics, more specifically including descriptive statistics, modeling construction, and numerical computation. Especially, several classical methods related to mathematical statistics in decision-making process will be detailly introduced, such as AHP, TOPSIS, RSR, etc. Whilst some commonly used mathematical software package such as MATLAB, SPSS, and LINGO will also introduced in the course of the statistics learning. Moreover, the research apprentics will introduced the usage of EndNote, a commercial reference management software package, used to manage bibliographies and references when writing essays and articles. Overall, by attending this program, the apprentics will have a large improvement in discovering, analyzing and solving practical problems from the mathematical perspective, that are closely related to their future studies.

Session 1: Introduction to statistics, modeling, and computation
Task 1: Summarize the overview (data, methodology, results) of the recommended papers, install some

software packages.
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Session 3: Analytic Hierarchy Process (AHP) Session 4: Entropy method

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Session 6: Rank-sum ratio

Highlights: RSR, short for Rank-sum ratio, is a statistic analysis method originally proposed by F.T. Tian in 1993. It integrates the strongpoints of classical parametric estimations and modern nonparametric estimations. Since its first introduction in 1988, RSR has been quickly recognized as a powerful and promising statistical analysis research tool for mathematical modeling of operational processes. In this session, the apprentics will have a comprehensive understanding in the application of RSR for grouping in monitoring process.

Session 7: Clustering Analysis (CA) ............

Task 7: Apply the CA to solve a grouping problem related to a specific project (benchmarking road safety).

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