Tim Hesterberg is a Senior Data Scientist at Google. He previously worked at Insightful, Franklin & Marshall College, and Pacific Gas & Electric Co. He received his Ph.D. in Statistics from Stanford University, under Brad Efron, and is a Fellow of the American Statistical Association. He is author of the Resample package for R, Chihara and Hesterberg, Mathematical Statistics with Resampling and R (2018), and What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum, The American Statistician 2015.
See resampling docs on his personal website.
We begin with a graphical approach to bootstrapping and permutation testing, illuminating basic statistical concepts of standard errors, confidence intervals, p-values and significance tests.
We 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, so we can do better statistics, e.g. use robust methods like medians, trimmed means, or robust regression. They can help clients understand statistical variability. And some of the methods are more accurate than standard methods.
Practicing statisticians and students. This is not a highly technical course.
- Introduction to Bootstrapping
- General procedure
- Why does bootstrapping work?
- Sampling distribution and bootstrap distribution
- Bootstrap Distributions and Standard Errors
- Sample mean
- Other statistics
- Simple confidence intervals
- How Accurate Is a Bootstrap Distribution?
- Bootstrap Confidence Intervals
- More accurate
- Significance Testing Using Permutation Tests
- Variety of statistics
- What can go wrong
- More sampling methods
- Stratified sampling
- Finite population
- Time series