## 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.

## Prerequisites

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

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.

Do NOT email your assignments.

Homework 1 due Thurs Sept 3 by 3:00

Homework 2 due Thurs Sept 17 by 3:00

Homework 3 due Thurs Oct 1 by 3:00

Homework 4 due Thurs Oct 8 by 3:00

Homework 5 due Thurs Oct 22 by 3:00

Homework 6 due Thurs Oct 29 by 3:00

Homework 7 due Thurs Nov 5 by 3:00

Homework 8 due Thurs Nov 19 by 3:00

Homework 9 due Thurs Dec 3 by 3:00

Practice Problems for Test 3

## 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

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

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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 1Inequalities September 7 NO CLASS Inequalities, O_P VC Theory September 14 Convergence Convergence Homework 2Convergence September 21 Convergence TEST ISufficiency September 28 Likelihood Point Estimation Homework 3Minimax Theory October 5 Minimax Theory Asymptotics Homework 4Asymptotics October 12 Asymptotics Review TEST IIOctober 19 Asymptotics Asymptotics Homework 5MID-SEMESTER BREAK October 26 Asymptotics Asymptotics Homework 6Hypothesis Testing November 2 Testing Testing Homework 7Confidence Intervals November 9 Nonparametric Inference Review TEST IIINovember 16 Nonparametric Inference The Bootstrap Homework 8Bayesian Inference November 23 NO CLASS NO CLASS November 30 Model Selection Model Selection Homework 9Regression December 7 Causation Causation Homework 10TBA