George Mason University
School of Information Technology and Engineering
Department of Applied and Engineering Statistics


STAT 789

Advanced Topics in Statistics: Bootstrapping and Other Resampling Methods

Summer Session, 2006
Tuesdays and Thursdays from 7:20 to 10:00 PM (starting June 6, other dates given below)

Location: room 133 of Innovation Hall


Instructor: Clifton D. Sutton

Contact Information (phone, fax, e-mail, etc.)
Office Hours: 6:00-7:00 PM on class nights


Text:

An Introduction to the Bootstrap, by Efron and Tibshirani


Prerequisite:

permission of instructor, but essentially STAT 554 (or equivalent); students need to have access to a computer on which they can download and install software


Description:

This course will cover most of the material in the book An Introduction to the Bootstrap, by Efron and Tibshirani. The main focus will be on bootstrapping, but related methods like jackkniffing and cross-validation will also be covered. (Bootstrapping is a computer-intensive nonparametric technique for making statistical inferences, like estimating the standard error and bias of point estimators, obtaining confidence intervals for distribution measures (such as the mean), doing tests of hypotheses, and estimating measures of prediction error.) The software emphasized will be R (which can be downloaded for free).


Approximate week-by-week content:

[1] Tu June 6:
introduction; the empirical distribution function; the plug-in principle; standard errors and estimated standard errors; using R (Ch. 1-5 of E&T)
[2] Th June 8:
the bootstrap estimate of standard error (Ch. 6-7 of E&T)
[3] Tu June 13:
dealing with more complicated data structures; bootstrapping and regression models (Ch. 8-9 of E&T)
[4] Th June 15:
estimates of bias; jackkkniffing (Ch. 10-11 of E&T)
[5] Tu June 20:
confidence intervals based on bootstrap tables and bootstrap percentiles (Ch. 12-13 of E&T)
[6] Th June 22:
better bootstrap confidence intervals (Ch. 14 of E&T)
[7] Tu June 27:
permutation tests; bootstrap hypothesis testing (Ch. 15-16 of E&T)
[8] Th June 29:
cross-validation and other estimates of prediction error (Ch. 17 of E&T)
[**] Tu July 4:
(No class due to 4th of July holiday break
[9] Th July 6:
adaptive estimation and calibration (Ch. 18 of E&T)
[10] Tu July 11:
assessing error in bootstrap estimates; geometrical representations for bootstrapping and jackkniffing (Ch. 19-20 of E&T)
[11] Th July 13:
an overview of parametric and nonparametric inference (Ch. 21 of E&T)
[12] Tu July 18:
more about bootstrap confidence intervals (Ch. 22 of E&T)
[13] Th July 20:
efficient bootstrap computations (Ch. 23 of E&T)
[14] Tu July 25:
review
[**] Th July 27:
Final Exam (note: exam period is from 7:30 to 10:15 PM)

Grading:


Additonal Comments: