Logistics

Time and Place: Mon,Wed,Fri 11.00-11.50, TCSEQ -201

Prerequisites

One course in Probability or Statistics

Assignments

There will be four homeworks to hand in 20 % There will be three labs to hand in 20 %

and two bigger projects (midterm and final) 60 %

Projects

General instructions:
The main component of the projects will be matlab, or Splus/R functions that perform certain analyses and produce graphics, these functions should be emailed to me and hardcopies sent to the TA's.

Turn in the complementary, explanatory part of your project, (this will be larger as we go on in the term), as a printed word-processed text, if you use formulas you might want to use LaTEXwhich is available on the leland machines to which you should have access.

Teaching

Instructor: Susan Holmes. OFFICE HOUR: Wednesday at 2.30pm, in Seqoia Hall,102. By email appointment to susan@stat.stanford.edu.

TA's Kris Jennings, jennings@stat and Ilana Belitskaya, ilana@stat.stanford.edu

TA's office hours:
Kris Jennings: Monday, 2-3pm, Friday 1-2pm
Ilana Belitskaya: Tuesday 2-4pm

Course Web Site

This will contain a bulletin board, homeworks, course summary, project description list, reading list, links to useful sites with in particular Splus and matlab tutorials, software information, etc...

Address:http://www-stat.stanford.edu/~susan/courses/stat208/

Weekly consultation of the web site will be necessary and expected of all students.

Startup computer labs in Sequoia Hall

There will be a special hands-on lab to get you started on at the beginning of next week (maybe next Monday April 8th)

Outline

Exploratory and Confirmatory Data Analysis		week 1
Motivating Examples		week 1
	Easy Problems where other methods are available
	Hard Problems where this is the only game in town
Computational Aspects		week 2
	Monte Carlo Methods
	Balanced Bootstrap
	Complete Enumeration?
Theoretical Aspects		week 3
	The plugin principle
	Nonparametric and Parametric
Other resampling Methods		week 4
	The jackknife
	Cross Validation
	Monte Carlo Markov Chain
Confidence Regions		week 5
	Confidence Intervals
	Confidence Bands
	Multivariate bootstrap
Bootstrapping for regression		week 6
	Bootstrapping the rows
	Bootstrapping the residuals
	Multivariate regression and pitfalls
Nonparametric Hypotheses Testing		week 7
	With the bootstrap
	With permutations
Better bootstraps		week 8
	Jackknife-after-Bootstrap
	Bootstrap-after-Bootstrap
	Corrected Bootstrapping
	Theory:pivotal statistics
Dependent Data		week 9
	Block bootstrap for time series
	Spatial Data

Software Matlab or R or Splus, as you wish.
Text Efron B., and Tibshirani R. (1993), An Introduction to the Bootstrap, Chapman and Hall.