## 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

TA:TBA

Office hours:TBA

Place:TBA

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.

## Prerequisites

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

syllabusincludes information about assignments, exams and grading.

## Assignments

Assignments are due onThursdays at 3:00 p.m.Hand in the assignment in the homework deposit box outside Baker Hall 132. Make surewrite 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.

Homework 1 due Thurs Sept 1 by 3:00

## Solutions

## Lecture Notes

Download the notes and bring them to class.

Lecture Notes 1

Lecture Notes 2

Lecture Notes 3

Course Calendar

Week of: Mon Wed Thursday Friday August 29 Review Review, Inequalities Homework 1Inequalities September 5 NO CLASS Inequalities, O_P TEST ISeptember 12 VC Theory Convergence Homework 2Convergence September 19 Convergence Convergence Sufficiency September 26 Likelihood Point Estimation Homework 3Minimax Theory October 3 Minimax Theory Asymptotics Homework 4Asymptotics October 10 Asymptotics Review TEST IIOctober 17 Testing Testing Homework 5MID-SEMESTER BREAK October 24 Testing Confidence Intervals Homework 6Confidence Intervals October 31 Nonparametric Nonparametric Homework 7Bootstrap November 7 Bootstrap Bayesian Inference TEST IIINovember 14 Prediction Prediction Homework 8Model Selection November 21 NO CLASS NO CLASS November 28 Model Selection Model Selection Homework 9Regression December 5 Causation Causation Homework 10TBA