## 10-705/36-705 Intermediate Statistics

Last Year's Final Exam

## Final Exam: Friday Dec 14 5:30 - 8:30

Instructor:Larry Wasserman

Time:MWF 12:30 - 1:20

Place:GHC 4307

Office Hour:Mondays 1:30 - 2:30 Baker Hall 228a

TA:Wanjie Wang (wwang@stat.cmu.edu)

Office hours:Tuesdays 4:00-5:00

Place:FMS 320

TA:Haijie (Jay) Gu (haijieg@cs.cmu.edu)

Office hours:Wednesdays 3:30-4:30

Place:GHC 8008

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 5 -- 10 from Casella and Berger plus a lot of supplementary material.

This course is excellent preparation for advanced work in statistics and machine learning.

Textbook:Casella, G. and Berger, R. L. (2002). Statistical Inference, 2nd ed.

Other Recommended Texts:

Wasserman, L. (2004). All of Statistics: A concise course in statistical inference.

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 1-4 of Casella and Berger (basic probability theory).The

syllabusincludes 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.

You can also email them to her. Send a PDF file to: mcshane@stat.cmu.edu. Make sure you put "Stat 705 Assigment" in the subject line.

Homework 1 due Thurs Sept 6 by 3:00

Homework 2 due Thurs Sept 20 by 3:00

Homework 3 due Thurs Sept 27 by 3:00

Homework 4 due Thurs Oct 4.Warning! Long homework:by 3:00

Homework 5 due Thurs Oct 18 by 3:00

Homework 6 due Thurs Oct 25 by 3:00

Homework 7 due Thurs Nov 1 by 3:00

Homework 8 due Thurs Nov 15 by 3:00

Homework 9 due Thurs Nov 29 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

Test1 1 Solutions

Test1 2 Solutions

Test1 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

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 27 Review Review, Inequalities Inequalities September 3 NO CLASS Inequalities, O_P Homework 1VC Theory September 10 Convergence Convergence Test ISeptember 17 Convergence Convergence Homework 2Sufficiency September 24 Likelihood Point Estimation Homework 3Minimax Theory October 1 Minimax Theory Asymptotics Homework 4Asymptotics October 8 Asymptotics Review Test IIOctober 15 Testing Testing Homework 5MID-SEMESTER BREAK October 22 Testing Testing Homework 6Confidence Intervals October 29 Nonparametric Nonparametric Homework 7Bootstrap November 5 Bootstrap Review Test IIINovember 12 Bayesian Inference Bayesian Inference Homework 8Bayesian Inference November 19 NO CLASS NO CLASS NO CLASS November 26 Prediction Prediction Homework 9Model Selection December 3 Model Selection Causation Causation