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 HoursTuesdays 12:30-2:30 Giuseppe Vinci (email@example.com) Wean Hall 8110 Tuesdays 5:30-6:30 Michael Spece (firstname.lastname@example.org) UC Second floor (tables above the pool) Wednesdays 1:30-3:30 Ed McFowland (email@example.com) Hamburg Hall 245 Wednesdays 5:00-6:00 Yen-Chi Chen (firstname.lastname@example.org) Wean hall 8110 Wednesdays 6:00-7:00 Federico Gonzalez (email@example.com) FMS 320 Wednesdays 7:15-8:15 Michael Spece (firstname.lastname@example.org) UC Second floor (tables above the pool) Thursdays 12:00-1:00 Jisu Kim (email@example.com) FMS 320 Course Assistant: Mari-Alice McShane firstname.lastname@example.org Office: Baker Hall 229K
Course descriptionThis 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.
PrerequisitesI assume that you know the material in Chapters 1-3 of of the book (basic probability) are familiar to you.
The syllabus includes information about assignments, exams and grading.
AssignmentsAssignments are due on Thursdays 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
SolutionsHomework 1 Solutions Homework 2 Solutions Homework 3 Solutions Homework 4 Solutions Homework 5 Solutions Homework 6 Solutions Test 1 Solutions
Lecture NotesDownload 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
Week of: Mon Wed Thursday Friday August 25 Review Review, Inequalities Inequalities September 1 NO CLASS Inequalities, O_P Homework 1 VC Theory September 8 Convergence Convergence Test I September 15 Convergence Convergence Homework 2 Sufficiency September 22 Likelihood Point Estimation Homework 3 Minimax Theory September 29 Minimax Theory Asymptotics Homework 4 Asymptotics October 6 Asymptotics Testing Homework 5 Testing October 13 Testing Testing Homework 6 MID-SEMESTER BREAK October 20 Confidence Intervals Review TEST II October 27 Nonparametric Nonparametric Homework 7 Bootstrap November 3 Bootstrap Bayesian Inference Homework 8 Bayesian Inference November 10 Prediction Prediction Homework 9 Model Selection November 17 Model Selection Review Test III November 24 NO CLASS NO CLASS NO CLASS December 1 Causation Causation Homework 10 Graphical Models