STA 332 Statistical Inference
Duke University Fall 2026
Below is a prospective outline for the course. Exam dates are firm, quiz dates will be announced later, and topics may change with advance notice.
| WEEK | DATE | PREPARE | TOPIC | MATERIALS | DUE/EVENT |
|---|---|---|---|---|---|
| 1 | Tue, Aug 25 |
[DS] Sec 7.1, [Wass] Ch 6 |
Lec 1: Intro to statistical inference | Notes | |
| Thu, Aug 27 |
[Wass] Ch 2,3 |
Lec 2: Probability review | Notes | ||
| Sufficiency and Data Reduction | |||||
| 2 | Tue, Sep 1 |
Berkeley notes, [DS] Sec 7.7, 7.8 |
Lec 3: Sufficiency | ||
| Thu, Sep 3 |
Berkeley notes, [DS] Sec 7.8 |
Lec 4: Minimal sufficiency | Notes for Lec 3 and 4 | Quiz 1 | |
| Sun, Sep 6 | HW1 | ||||
| 3 | Tue, Sep 8 | Berkeley notes, MIT OCW notes | Lec 5: Exponential families, ancillarity, location-scale families | Notes | |
| Point estimation | |||||
| Thu, Sep 10 |
[Wass] Sec 9.2–9.3; [DS] Sec 7.5, & “Method of Moments” in Sec 7.6 |
Lec 6: Constructing estimators: method of moments, MLE | Preliminary notes | ||
| 4 | Tue, Sep 15 |
[Wass] Sec 9.6, 12.1, and Sec 12.2 through Example 12.3; [DS] Theorem 7.8.3 |
Lec 7: Estimator properties and risk | HW2 | |
| Thu, Sep 17 |
[DS] Sec 8.7 |
Lec 8: Unbiased estimation and the UMVUE problem | |||
| 5 | Tue, Sep 22 | To be posted | Lec 9: Rao–Blackwellization | ||
| Thu, Sep 24 | To be posted | Lec 10: Completeness | |||
| 6 | Tue, Sep 29 | To be posted | Lec 11: Lehmann–Scheffe and UMVUE construction | ||
| Thu, Oct 1 | To be posted | Lec 12: Lehmann–Scheffe proof and applications | |||
| 7 | Tue, Oct 6 | — | Midterm 1 | ||
| Thu, Oct 8 | To be posted | Lec 13: Fisher information and Cramer–Rao | |||
| 8 | Tue, Oct 13 | — | No class (Fall Break) | ||
| Hypothesis Testing | |||||
| Thu, Oct 15 | To be posted | Lec 14: Testing principles | |||
| 9 | Tue, Oct 20 | To be posted | Lec 15: Optimal tests: most-powerful tests, Neyman–Pearson | ||
| Thu, Oct 22 | To be posted | Lec 16: Neyman–Pearson in practice | |||
| 10 | Tue, Oct 27 | To be posted | Lec 17: UMP tests and monotone likelihood ratios | ||
| Thu, Oct 29 | To be posted | Lec 18: Likelihood-ratio tests (LRTs) | |||
| Large Sample Theory | |||||
| 11 | Tue, Nov 3 | To be posted | Lec 19: Modes of convergence | ||
| Thu, Nov 5 | To be posted | Lec 20: LLN, CLT, and Slutsky | |||
| 12 | Tue, Nov 10 | To be posted | Lec 21: Delta method and estimator asymptotics | ||
| Thu, Nov 12 | To be posted | Lec 22: MLE asymptotics | |||
| 13 | Tue, Nov 17 | To be posted | Lec 23: Wilks’ theorem and large-sample LRTs | ||
| Thu, Nov 19 | — | Midterm 2 | |||
| Confidence intervals | |||||
| 14 | Tue, Nov 24 | To be posted | Lec 24: Foundations | ||
| Thu, Nov 26 | — | No class (Thanksgiving) | |||
| 15 | Tue, Dec 1 | To be posted | Lec 25: Exact construction | ||
| Thu, Dec 3 | To be posted | Lec 26: Asymptotic methods | |||
| 16 | Thu, Dec 10 | — | Final exam |