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Bootstrap Methods

Bootstrap Methods

This course will teach you the basic theory and application of the bootstrap family of procedures with the emphasis on applications.


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

Dr. Robert LaBudde

Dr. Robert LaBudde is president and founder of Least Cost Formulations, Ltd., a mathematical software development company specializing in optimization and process control software for manufacturing companies. He has served on the faculties of the University of Wisconsin, Massachusetts Institute of Technology, Old Dominion University and North Carolina State University. Dr. LaBudde is currently Adjunct Professor of Statistics at Old Dominion University.

Course Syllabus

Week 1


  • 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
    • Nonparametric Regression
      • Cox Model
      • CART
      • Bootstrap Bagging
    • Time Series Analysis
      • Model-based vs block resampling
    • Bootstrap variants
      • Bayesian bootstrap
      • Smoothed bootstrap
      • Parametric bootstrap
      • Iterated bootstrap
    • 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


09/22/2023 to 10/20/2023


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

The course introduces the basic concepts and methods of resampling methods including bootstrap procedures and permutation with little or no complex theory or confusing notation.
  • Skill: Intermediate, Advanced
  • Credit Options: CEU
Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

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Bootstrap Methods

Additional Information


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.


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

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

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