Syllabus

Course Meeting Times

Lectures: 2 sessions / week, 1.5 hours / session

Recitations: 1 session / week, 1.5 hours / session

Prerequisite

18.02 Multivariable Calculus

Text

The text, which will be followed closely, is Buy at Amazon 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