10-705/36-705 Intermediate Statistics

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

Office Hour: Mondays 1:30 - 2:30 Baker Hall 232G

TA Office Hours

Tuesdays 12:30-2:30 Giuseppe Vinci (gvinci@andrew.cmu.edu) Wean Hall 8110
Tuesdays 5:30-6:30 Michael Spece (mspeceib@stat.cmu.edu) UC Second floor (tables above the pool)

Wednesdays 1:30-3:30 Ed McFowland (mcfowland@cmu.edu) Hamburg Hall 245
Wednesdays 5:00-6:00 Yen-Chi Chen (yenchic@andrew.cmu.edu) Wean hall 8110
Wednesdays 6:00-7:00 Federico Gonzalez (fgonzale@andrew.cmu.edu) FMS 320
Wednesdays 7:15-8:15 Michael Spece (mspeceib@stat.cmu.edu) UC Second floor (tables above the pool)

Thursdays 12:00-1:00 Jisu Kim (jisuk1@andrew.cmu.edu) FMS 320

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 a lot of 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.

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.


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
BONUS: Short summary of tests and confidence intervals
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 25 Review   Review, Inequalities     Inequalities
September 1 NO CLASS   Inequalities, O_P   Homework 1   VC Theory
September 8 Convergence Convergence     Test I
September 15 Convergence Convergence Homework 2   Sufficiency
September 22 Likelihood Point Estimation Homework 3   Minimax Theory
September 29 Minimax Theory Asymptotics Homework 4   Asymptotics
October 6 Asymptotics Testing Homework 5   Testing
October 13 Testing Testing Homework 6   MID-SEMESTER BREAK
October 20 Confidence Intervals Review   TEST II
October 27 Nonparametric Nonparametric Homework 7 Bootstrap
November 3 Bootstrap Bayesian Inference Homework 8 Bayesian Inference
November 10 Prediction Prediction Homework 9 Model Selection
November 17 Model Selection Review Test III
December 1 Causation Causation Homework 10 NO CLASS