Mathematical Statistics I
This course offers a systematic introduction to the theory of mathematical statistics, based on the first three chapters of Shao's Mathematical Statistics. It begins with the essential probabilistic foundations and then develops the core principles of statistical inference, including sufficiency and completeness, loss and risk, and the main approaches to point estimation, hypothesis testing, and confidence sets. Topics in unbiased estimation are also covered, with attention given to both finite-sample and asymptotic properties of estimators and tests.
讲师
日期
2026年09月02日 至 11月25日
位置
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| 周三 | 13:30 - 16:55 | A3-3-201 | ZOOM 14 | 712 322 9571 | BIMSA |
修课要求
Fundamentals of Probability Theory
课程大纲
1. Preliminaries
2. Foundations of Probability
3. Asymptotic Theory and Conditioning
4. Statistical Models and Data Reduction
5. Principles of Statistical Inference
6. Theory of Unbiased Estimation
7. Estimation in Linear and Nonparametric Models
2. Foundations of Probability
3. Asymptotic Theory and Conditioning
4. Statistical Models and Data Reduction
5. Principles of Statistical Inference
6. Theory of Unbiased Estimation
7. Estimation in Linear and Nonparametric Models
参考资料
[1] Shao, J., Mathematical Statistics, 2nd ed., New York: Springer, 2003.
[2] 茆诗松, 王静龙, 濮晓龙, 高等数理统计, 第3版, 北京: 高等教育出版社, 2022.
[2] 茆诗松, 王静龙, 濮晓龙, 高等数理统计, 第3版, 北京: 高等教育出版社, 2022.
听众
Advanced Undergraduate
, Graduate
视频公开
公开
笔记公开
不公开
语言
中文
讲师介绍
刘思序于2019年获得北京大学博士学位，之后在清华大学任博士后，并于2022年加入BIMSA任助理研究员。主要研究方向为动力系统与遍历论，统计试验设计。