Bootstrap Methods
This course will teach you the basic theory and application of the bootstrap family of procedures with the emphasis on applications.
Overview
This course, designed for Statisticians and data analysts who perform statistical inference or need to assess uncertainty in their data, covers the basic theory and application of the bootstrap family of procedures with an emphasis on applications. It also illustrates bootstrap for regression and time series procedures. Students should be familiar with introductory statistics, and must have a working installation of the R statistical software package.
- Intermediate, Advanced
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
After taking this course, participants will be able to use the bootstrap procedure to assess bias and variance, test hypotheses, and produce confidence intervals.
- Use the bootstrap to estimate bias
- Construct confidence intervals using the bootstrap
- Apply the bootstrap to linear regressions
- Apply the bootstrap to time series analysis
- Make appropriate use of bootstrap variants
Who Should Take This Course
Statisticians and data analysts who perform statistical inference, or need to assess uncertainty in their data. Those working with data that does not meet the distributional requirements of standard statistical procedures, or with unusual statistics or complex estimators will find the course particularly useful.
Our Instructors
Dr. Robert LaBudde
Course Syllabus
Week 1
Introduction
- Wide range of application
- Historical notes
- Bias estimation
- Efron’s patch data example
- Estimating other parameters of a distribution
Week 2
Parameter Estimation
- Bias estimation (continued)
- Error rate estimation problems
- Confidence intervals and hypothesis test
- Percentile method confidence intervals
- Higher order bootstrap confidence intervals
- A 1-1 relationship between confidence intervals and hypothesis tests
- Problems with bootstrap confidence intervals for variances
Week 3
Regression, Time Series, Which Methods?
- Linear Regression, bootstrap residuals or vectors
- Non-linear Regression
- A Quasi-optical experiment
- Non-linear Regression
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- Nonparametric Regression
- Cox Model
- CART
- Bootstrap Bagging
- Nonparametric Regression
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- Time Series Analysis
- Model-based vs block resampling
- Time Series Analysis
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- Bootstrap variants
- Bayesian bootstrap
- Smoothed bootstrap
- Parametric bootstrap
- Iterated bootstrap
- Bootstrap variants
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- Number of repetitions (replications)
Week 4
Special Topics, Bootstrap Failures and Remedies
- Spatial data: kriging
- Subset selection
- Examples of Gong and Gunter
- p-value adjustment
- Process capability indices
- Bioequivalence
- Failure Due to Small Sample Size
- Failure Due to Infinite Moments and Remedy (introducing m-out-of-n bootstrap)
- Failure Due to Estimating Extremes and Remedies
Class Dates
2023
Instructors:
Prerequisites
Use of statistical software is important in this course. Please read the software section below for additional information on software requirements.
After your self-assessment, review our course, Introduction to Resampling, which provides a non-statistician’s perspective on basic bootstrapping, and then decide which course is right for you.
Private: Introduction to Resampling Methods
- Skill: Intermediate, Advanced
- Credit Options: CEU
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Additional Information
Homework
Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.
In addition to assigned readings, this course also has and an exam.
Course Text
The required text for this course is An Introduction to Bootstrap Methods with Applications to R by Michael Chernick and Robert LaBudde.
Software
You must have a copy of R for the course. If you are not familiar with R, you should consider taking our “Introduction to R” courses:
Supplemental Information
Literacy, Accessibility, and Dyslexia
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