| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
| 提供修課同學對大部分的統計方法能知道其中的原理，更進一步的學會如何發展擁有好性質的估計方法以及如何評估估計方法之間的優劣。這一部份將主要著重在假設檢定。 |
| 6.Theory of Mathematical Analysis, Statistics, and Mechanics |
|
|
|
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| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
[W1] Sufficiency and factorization theorem. Exponential family; functions of a parameter |
| Week 2 |
[W2] Minimal sufficiency, ancillary, completeness, independence (Basu’s theorem)
|
| Week 3 |
[W3] Most powerful test, likelihood ratio test, multivariate extensions |
| Week 4 |
[W4] Sequential probability ratio test (SPRT) |
| Week 5 |
[W5] Other tests |
| Week 6 |
[W6] Quadratic forms; onw-way ANOVA |
| Week 7 |
[W7] noncentral chi-square, non-central t, and non-central F distributions |
| Week 8 |
[W8] Multiple comparisons ; ANOVA and regression |
| Week 9 |
[W9] More on properties of quadratic forms |
| Week 10 |
[W10] Midterm exam |
| Week 11 |
[W11] signed rank test and ranksum test |
| Week 12 |
[W12] Prior and posterior distributions; |
| Week 13 |
[W13] Bayes procedure
|
| Week 14 |
[W14] Bayes decision making
|
| Week 15 |
[W15] Gibb’s sampler and bootstrapping |
| Week 16 |
[W16] Modern Bayesian inferences; Final exam |
self-directed
learning |
03.Preparing presentations or reports related to industry and academia.
彈性自主學習
主題1: Case study for Bayes inferences
主題2: bootstrapping and EM algorithm |
|
| Evaluation |
| 考試100% |
| Textbook & other References |
| Hogg, McKean, and Craig, Mathematical Statistics, 7th ed. |
| Teaching Aids & Teacher's Website |
| 上課講義 |
| Office Hours |
| 星期四 11:00~12:00 |
| Sustainable Development Goals, SDGs(Link URL) |
| include experience courses：N |
|