36-705 Intermediate Statistics

Hand in your assignments! Do not send them by email!

Instructor: Larry Wasserman
Time: MWF 12:30 - 1:20
Place: Baker Hall A51

Office Hour: Mondays 1:30 - 2:30 Baker Hall 132F

TA Information:
TA: Giuseppe Vinci (gvinci@andrew.cmu.edu)
Office hours: Tuesdays 3:00 - 4:00
Place: Baker Hall 132M

TA: Jisu Kim (jisuk1@andrew.cmu.edu)
Office hours: Thursdays 10:00 - 11:00
Place: Baker Hall 132M

TA: Daren Wang (darenw@andrew.cmu.edu)
Office hours: Wednesdays 3:00 - 4:00
Place: Baker Hall 132M

TA: Lawrence Wang (lawrencw@andrew.cmu.edu)
Office hours: Wednesdays 4:00 - 5:00
Place: Baker Hall 132M

Course Assistant: Mari-Alice McShane mcshane@stat.cmu.edu
Office: Baker Hall 229K

Course description

This course will cover the fundamentals of theoretical statistics. We will cover Chapters 1 -- 12 from the text plus some supplementary material. This course is excellent preparation for advanced work in statistics and machine learning.

Textbook: Wasserman, L. (2004). All of Statistics: A concise course in statistical inference.

Cartoons!


Last Year's Exam




Other Recommended Texts:

Casella, G. and Berger, R. L. (2002). Statistical Inference, 2nd ed.
Bickel, P. J. and Doksum, K. A. (1977). Mathematical Statistics.
Rice, J. A. (1977). Mathematical Statistics and Data Analysis, Second Edition.

Prerequisites

I assume that you know the material in Chapters 1-3 of of the book (basic probability) are familiar to you. If not you should take 36-700.

The syllabus includes information about assignments, exams and grading.

Assignments

Assignments are due on Thursdays at 3:00 p.m. Hand in the assignment to Mari-Alice McShane in Baker Hall 229K.
Do NOT email your assignments.

Practice Problems for Test 3


Solutions

Homework 1 Solutions
Homework 2 Solutions
Homework 3 Solutions
Homework 4 Solutions
Homework 5 Solutions
Homework 6 Solutions
Homework 7 Solutions
Homework 8 Solutions
Homework 9 Solutions
Homework 10 Solutions
Test 1 Solutions
Test 2 Solutions
Test 3 Solutions

Lecture Notes

Download the notes and bring them to class.
Lecture Notes 1
Lecture Notes 2
Lecture Notes 3
Lecture Notes 4
Lecture Notes 5
Lecture Notes 6
Lecture Notes 7
Lecture Notes 8
Lecture Notes 9
Lecture Notes 10
Lecture Notes 11
Lecture Notes 12
Lecture Notes 13
Lecture Notes 14
Lecture Notes 15
Lecture Notes 16
Lecture Notes 17

Course Calendar
Week of: Mon Wed Thursday Friday
August 31 Review   Review, Inequalities   Homework 1   Inequalities
September 7 NO CLASS   Inequalities, O_P     VC Theory
September 14 Convergence Convergence Homework 2     Convergence
September 21 Convergence TEST I   Sufficiency
September 28 Likelihood Point Estimation Homework 3   Minimax Theory
October 5 Minimax Theory Asymptotics Homework 4   Asymptotics
October 12 Asymptotics Review   TEST II
October 19 Asymptotics Asymptotics Homework 5   MID-SEMESTER BREAK
October 26 Asymptotics Asymptotics Homework 6   Hypothesis Testing
November 2 Testing Testing Homework 7 Confidence Intervals
November 9 Nonparametric Inference Review TEST III
November 16 Nonparametric Inference The Bootstrap Homework 8 Bayesian Inference
November 23 NO CLASS NO CLASS
November 30 Bayesian Inference Prediction Homework 9   Model Selection
December 7 Causation Causation Homework 10   TBA