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 132F

TA Information:
TA: Collin Eubanks ceubanks@andrew.cmu.edu
Office hours: Wednesdays 4:00-5:00
Place: Porter Hall 117

TA: Purvasha Chakravarti pchakrav@andrew.cmu.edu
Office hours: Wednesdays 3:00-4:00
Place: Porter Hall 117

TA: Zongge Liu zonggel@andrew.cmu.edu
Office hours: Tuesdays 2:00-3:00
Place: Porter Hall 117

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.
Van der Vaart, A. (2000). Asymptotic Statistics.


I assume that you know the material in Chapters 1-3 of of the book (basic probability) are familiar to you. If not, then 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 in the homework deposit box outside Baker Hall 132. Make sure write the course number on your assignment in large clear letters.
No late assignments will be accepted. If you need an extension due to illness, email me BEFORE the deadline.


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**** Practice Test for Test 2 ****


Homework 1 Solutions
Test 1 Solutions
Homework 2 Solutions
Homework 3 Solutions
Homework 4 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

Course Calendar
Week of: Mon Wed Thursday Friday
August 29 Review   Review, Inequalities   Homework 1   Inequalities
September 5 NO CLASS   Inequalities, O_P     TEST I
September 12 VC Theory Convergence Homework 2     Convergence
September 19 Convergence Convergence   No Class
September 26 Likelihood Point Estimation Homework 3   Minimax Theory
October 3 Minimax Theory Asymptotics Homework 4   Asymptotics
October 10 Asymptotics Review   TEST II
October 17 Testing Testing Homework 5   MID-SEMESTER BREAK
October 24 Testing Confidence Intervals Homework 6   Confidence Intervals
October 31 Nonparametric Nonparametric Homework 7 Bootstrap
November 7 Bootstrap Bayesian Inference TEST III
November 14 Prediction Prediction Homework 8 Model Selection
November 28 Model Selection Model Selection Homework 9   Regression
December 5 Causation Causation Homework 10   TBA