## 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

syllabusincludes information about assignments, exams and grading.

## Assignments

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

Homework 1 due Thurs Sept 4 by 3:00

Homework 2 due Thurs Sept 18 by 3:00

Homework 3 due Thurs Sept 25 by 3:00

Homework 4 due Thurs Oct 2 by 3:00

Homework 5 due Thurs Oct 9 by 3:00

Homework 6 due Thurs Oct 16 by 3:00

Homework 7 due Thurs Oct 30 by 3:00

Homework 8 due Thurs Nov 6 by 3:00

Homework 9 due Thurs Nov 13 by 3:00

Homework 10 due Thurs Dec 4 by 3:00

## 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

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 1VC Theory September 8 Convergence Convergence Test ISeptember 15 Convergence Convergence Homework 2Sufficiency September 22 Likelihood Point Estimation Homework 3Minimax Theory September 29 Minimax Theory Asymptotics Homework 4Asymptotics October 6 Asymptotics Testing Homework 5Testing October 13 Testing Testing Homework 6MID-SEMESTER BREAK October 20 Confidence Intervals Review TEST IIOctober 27 Nonparametric Nonparametric Homework 7Bootstrap November 3 Bootstrap Bayesian Inference Homework 8Bayesian Inference November 10 Prediction Prediction Homework 9Model Selection November 17 Model Selection Review Test IIINovember 24 NO CLASS NO CLASS NO CLASS December 1 Causation Causation Homework 10NO CLASS