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 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.
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.

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