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.

Prerequisites

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

Assignments are due on Thursdays at 3:00 p.m. Hand in the assignment to Mari-Alice McShane in Baker Hall 229K.

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
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
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
November 24 NO CLASS NO CLASS   NO CLASS
December 1 Causation Causation Homework 10 NO CLASS