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


One course in Probability or Statistics


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 %


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.


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

TA's Kris Jennings, jennings@stat and Ilana Belitskaya,

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...


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)


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
  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.

next up previous index
Next: Labs and Homeworks Up: Introduction to the Bootstrap: Previous: Introduction to the Bootstrap:   Index
Susan Holmes 2002-04-25