Course Meeting Times
Lectures: 2 sessions / week, 1.5 hours / session
Recitations: 1 session / week, 1.5 hours / session
Prerequisite
Text
The text, which will be followed closely, is 
Casella, George, and Roger L. Berger. Statistical Inference. Cengage Learning, 2001. ISBN: 9780534243128.
This book covers all of the material of the course and, in addition, provides many problems for practice as well as excellent references.
Grading
There will be a midterm (worth 35%). There will be 6 problem sets. This will constitute 15% of the grade. The solution to this problem will be posted after the due date. No late assignments will be accepted. All other problems are for your own study; the solutions to them won't be posted, but will be discussed during the sections. One problem from the problem sets will appear on the mid-term exam.
Calendar
| SES # | TOPICS | KEY DATES |
|---|---|---|
| 1 | Introduction, Short summary of probabilistic concepts, Normal distribution. | |
| 2 | Limit theorems | Problem set 1 (intro & convergence) is given |
| 3 | Sample, histograms, sample moments, likelihood function | |
| 4 | Sufficient statistics | Problem set 1 is due; Problem set 2 (estimation & sufficient statistics) is given |
| 5 | Point estimators, method of moments | |
| 6 | Efficient estimators, Rao–Cramer bound | Problem set 2 is due; Problem set 3 (Information, MLE) is given |
| 7 | Large sample properties of MLE | |
| 8 | Bayesian concepts | Problem set 3 is due; Problem set 4 (Bayesian, testing) is given |
| 9 | Testing concepts | |
| 10 | Testing, UMP, Neyman–Pearson lemma | Problem set 4 is due; Problem set 5 (on testing) is given |
| 11 | Large sample tests | |
| 12 | Confidence sets construction | Problem set 5 is due; Problem set 6 (on confidence sets) is given |
| Midterm | Problem set 6 is due |