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.

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.


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 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


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

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 30 Model Selection Model Selection Homework 9   Regression
December 7 Causation Causation Homework 10   TBA