Nonparametric Statistics
This course will first cover All of Nonparametric Statistics by Wasserman and then turn to the latter chapters (7 onwards) from ESL. All theoretical material from ESL I'll accompany with Python labs from ISL.
讲师
日期
2024年09月10日 至 12月03日
位置
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| 周二,周四 | 17:55 - 20:05 | Online | ZOOM 05 | 293 812 9202 | BIMSA |
修课要求
This course will assume you attended my previous two courses, which covered the book All of Statistics by Wasserman. In particular, I'll assume you have a broad theoretical basis in statistics and probability, understanding concepts such as random variables, CDFs, pdfs, convergence in probability/distribution, parametric and nonparametric models, the bootstrap, maximum likelihood estimation (eg Chapter 1-9 of Wasserman), linear and logistic regression (eg Chapter 3-4 of ESL or 13,22 of Wasserman). More specifically, this course will build upon the nonparametric methods introduced in Chapter 20-21 of Wasserman, so please read or revise those
参考资料
Wasserman, All of Nonparametric Statistics
James et al, Introduction to Statistical Learning
Efron and Tibshirani, An Introduction to the Bootstrap
Hastie et al, Elements of Statistical Learning
James et al, Introduction to Statistical Learning
Efron and Tibshirani, An Introduction to the Bootstrap
Hastie et al, Elements of Statistical Learning
听众
Graduate
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