Bootstrap Methods and Permutation Tests

COURSE OBJECTIVE

This full-day or half-day course is designed to introduce participants to Bootstrapping and Bootstrapping methods. The course takes a graphical approach to bootstrapping and permutation testing, illuminating basic statistical concepts of standard errors, confidence intervals, p-values and significance tests.

NUMBER OF DAYS:

Full-Day or Half-Day

COURSE OUTLINE

1. Introduction to Bootstrapping
2. General procedure
3. Why does bootstrapping work?
4. Sampling distribution and bootstrap distribution
5. Bootstrap Distributions and Standard Errors
6. Distribution of the sample mean
7. Bootstrap distributions of other statistics
8. Simple confidence intervals
9. Two-sample applications

HOW ACCURATE IS BOOTSTRAP DISTRIBUTION?

• Bootstrap Confidence Intervals
• Bootstrap percentiles as a check for standard intervals
• More accurate bootstrap confidence intervals
• Significance Testing Using Permutation Tests
• Two-sample applications
• Other settings
• Wider variety of statistics
• Variety of applications
• Examples where things go wrong, and what to look for
• Wider variety of sampling methods
• Stratified sampling, hierarchical sampling
• Finite population
• Regression
• Time series

COURSE HIGHLIGHTS

The course will consider a variety of statistics (mean, trimmed mean, regression, etc.), and a number of sampling situations (one-sample, two-sample, stratified, finite-population), stressing the common techniques that apply in these situations. We'll look at applications from a variety of fields, including telecommunications, finance, and biopharm.

These methods let us do confidence intervals and hypothesis tests when formulas are not available. This lets us do better statistics, e.g. use robust methods (we can use a median or trimmed mean instead of a mean, for example). They can help clients understand statistical variability. And some of the methods are more accurate than standard methods.

PARTICIPANTS WILL LEARN HOW TO USE RESAMPLING METHODS

• to compute standard errors,
• to check the accuracy of the usual Gaussian-based methods,
• to compute both quick and more accurate confidence intervals,
• for a variety of statistics and for a variety of sampling methods, and
• to perform significance tests in some settings.

COURSE INSTRUCTOR

Dr. Tim Hesterberg is a Senior Statistician at Google.  He previously worked at Insightful (S-PLUS), Franklin & Marshall College, and Pacific Gas & Electric Co.  He received his Ph.D. in Statistics from Stanford University, under Brad Efron.

Hesterberg is author of the "Resample" package for R and primary author of the "S+Resample" package for bootstrapping, permutation tests, jackknife, and other resampling procedures, is co-author of Chihara and Hesterberg "Mathematical Statistics with Resampling and R" (2011), and is lead author of "Bootstrap Methods and Permutation Tests" (2010), W. H. Freeman, ISBN 0-7167-5726-5, and technical articles on resampling.  See http://www.timhesterberg.net/bootstrap

Hesterberg is on the executive boards of the National Institute of Statistical Sciences and the Interface Foundation of North America (Interface between Computing Science and Statistics).

He teaches kids to make water bottle rockets, leads groups of high school students to set up computer labs abroad, and actively fights climate chaos.

AVAILABLE DATES

Want to see this courses take place in your area?

NISS can facilitate this course on any of the available dates below.  Please write to us at officeadmin@niss.org or call us at 202-862-4316.

WHY IS BOOTSTRAPPING IMPORTANT IN STATISTICS?

Resampling methods are important in statistical practice, but have been omitted or poorly covered in many old-style statistics courses. These methods are an important part of the toolbox of any practicing statistician. It is important when using these methods to have some understanding of the ideas behind these methods, to understand when they should or should not be used. They are not a panacea. People tend to think of bootstrapping in small samples, when they don't trust the central limit theorem. However, the common combinations of nonparametric bootstrap and percentile intervals are actually less accurate than t procedures. We discuss why, remedies, and better procedures that are only slightly more complicated. These tools also show how poor common rules of thumb are -- in particular, n >= 30 is woefully inadequate for judging whether t procedures should be OK.

SOFTWARE PACKAGES
R resample and boot packages.

HOW TO REGISTER
Call or email: You can call (202) 862-4316 or write to officeadmin@NISS.org for more information or to register