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Logistics
Index
Introduction to the Bootstrap:
Statistics 208
Logistics
Prerequisites
Assignments
Projects
Teaching
Course Web Site
Startup computer labs in Sequoia Hall
Outline
Labs and Homeworks
Labs
Lab 1, April 8th, 2002: to hand in before April 11th
Lab 2, April 17th, 2002, class time:11.00am: to hand in before April 20th
Homeworks
Homework 1, Due April 17th, 2002: to hand in before lab
Homework 2:due Friday April, 26th
Lectures
Situation of resampling in contemporary statistics
Fisher's Classical Paradigm
Tukey and Mallow's Exploratory-Confirmatory Paradigm
The underlying principle
The questions addressed
The bootstrap: Some Examples
A binomial example
Computing the bootstrap distribution for one sample
Comparing to a hypothetical parameter
Computing the bootstrap distribution for two samples
Without the computer
Some notation
Accuracy of the sample mean
The combinatorics of the bootstrap distribution
How many different bootstrap samples are there?
Which is the most likely bootstrap sample?
The
multinomial
distribution
Complete Enumeration
The original Gray code
Gray Codes for the Bootstrap
Gray codes for compositions.
Balanced Bootstraps
Monte Carlo
What is a
Monte Carlo
Method?
Antithetic Resampling
Importance Sampling
Theoretical Underpinnings
Statistical Functionals
Notions of Convergence
Why is the empirical cdf
a good estimator of F?
Generalized Statistical Functionals
The jackknife
The jackknife estimate of variance
Example: Patch Data
Cross Validation
Cross-Validation when there is a response variable
Confidence Intervals
Properties and Improvements
Studentized Confidence Intervals
Correlation Coefficient Example
Transformations of the parameter-matlab example
Variance Stabilizing Transformations
Correlation Coefficient
Bootstrap World
References
A whole statistical library in a website
Semi-numerical Algorithms
R/Splus
Matlab Based Books
Algorithmic Classics:
Index
Susan Holmes 2002-04-25
a good estimator of F?