Zhou Fan, Yale University, Spring 2025
Course schedule (tentative)
Unit 0 - Introduction and tools| Mon 1/13 | Course introduction, survey sampling | Rice 7.1-7.3 |
| Wed 1/15 | Probability review I | Rice 2.1-4.3 |
| Wed 1/22 | Probability review II | Rice 4.5, 6.1-6.2 |
| Fri 1/24 | Large-sample approximations, computer simulation | Rice 5.1-5.3 |
| Mon 1/27 | Null hypotheses, test statistics, p-values | Rice 9.7-9.8 |
| Wed 1/29 | z-tests and t-tests for one and two samples | Rice 6.3, 11.1-11.2 |
| Mon 2/3 | Nonparametric tests, permutation tests | Rice 11.2-11.3 |
| Wed 2/5 | Statistical power, Neyman-Pearson lemma | Rice 9.1-9.2 |
| Mon 2/10 | Effect size, power, and experimental design | Rice 11.3-11.4 |
| Wed 2/12 | Testing multiple hypotheses | ISLR 13.1-13.4 |
| Mon 2/17 | Parametric models and method of moments | Rice 8.1-8.4 |
| Wed 2/19 | Maximum likelihood estimation and optimization | Rice 8.5-8.5.1 |
| Mon 2/24 | Asymptotic normality and the delta method | Rice 4.6 |
| Wed 2/26 | Consistency and asymptotic normality of the MLE | Rice 8.5.2-8.5.3 |
| Mon 3/3 | Fisher information and the Cramer-Rao bound | Rice 8.7 |
| Wed 3/5 | The generalized likelihood ratio test | Rice 9.4-9.5 |
| Mon 3/10 - Fri 3/21: No class, spring recess | ||
| Mon 3/24 | Bayesian inference I | Rice 8.6-8.6.1 |
| Wed 3/26 | Bayesian inference II | Rice 8.6.2-8.6.3 |
| Mon 3/31 | Parameter estimation in misspecified models | |
| Wed 4/2 | Uncertainty quantification using the bootstrap | ISLR 5.2 |
| Mon 4/7 | Models with covariates and response | ISLR 2.1-3.1 |
| Wed 4/9 | Inference in simple linear regression | Rice 14.2, ISLR 3.1 |
| Mon 4/14 | Multiple linear regression | ISLR 3.2-3.3 |
| Wed 4/16 | Classification and logistic regression | ISLR 4.1-4.3 |
| Mon 4/21 | Generative models for classification | ISLR 4.4-4.5 |
| Wed 4/23 | Conformal prediction and cross-validation | ISLR 5.1 |