Bootstrap Methods

Brief Description:

This course covers the basic theory and application of the bootstrap family of procedures, with the emphasis on applications.

Instructor(s):
Level: intermediate/advanced

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.

See also: Introduction to Resampling Methods

Dates:
March 16, 2012 to April 13, 2012
bootsrap Click here to be reminded of future sessions of this course.

Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

Register Online -$499
Register Online -$399 (you must be affiliated with a college, university or high school)

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise. Multiple course registrations may be entitled to tuition discounts; read more.

Share This :

Bootstrap Methods

Aim of Course:

This course covers the basic theory and application of the bootstrap family of procedures, with the emphasis on applications. After taking this course, participants will be able to use the bootstrap procedure to assess bias and variance, test hypotheses, and produce confidence intervals. The bootstrap is illustrated also for regression and time series procedures. Basic and improved bootstrap procedures are covered.

Prerequisite(s):

• Survey of Statistics for Beginners, or Introduction to Statistics for Beginners
• Introduction to Statistics 1: Sampling and Inference
• Introduction to Statistics 2: Working with Bivariate Data)

If you are unclear as to whether you have mastered the requirements, test yourself with these placement exams here.

Introduction to Resampling, though not a prerequisite, will also be useful -- it provides a non-statistician's perspective on basic bootstrapping. Use of statistical software is important in this course -- please read the software section below for additional information on software requirements.

If you are not familiar with R, you should take one of statistics.com's "Introduction to R" courses.

Course Program:

SESSION 1: Introduction

• Wide range of application
• Historical notes
• Bias estimation
  • Efron's patch data example
  • Estimating other parameters of a distribution

SESSION 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

SESSION 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)

SESSION 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

Organization of the Course:

This course takes place over the internet, at statistics.com for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and you will receive individual feedback on your homework answers.

Credit:
Students come to The Institute for a variety of reasons:

1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. You may be enrolled in PASS (Program in Advanced Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).

As you begin the class, you will be asked to specify your category.

This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a record of course completion will be issued by Statistics.com, upon request.

Course Text:

The course text is An Introduction to Bootstrap Methods with Applications to R by Michael Chernick and Robert LaBudde, which can be ordered directly from Wiley here.  Wiley typically offers statistics.com customers up to 15% discount on this book (and all other statistics titles): enter the code aff15 in the Promotion Code field when prompted during checkout and click the Apply Discount button. (If you are located in Asia, the web procedure for your location may not accept this discount -- try calling your regional Wiley representative.)

Software:

This course has illustrations and exercises using R and with solutions in R.

Some of the problems can be done in Resampling Stats; all can be done in R. Resampling Stats is a special purpose program that can be downloaded and tried ahead of time and learned as part of the course; a license extension will be provided to course participants on the first day of the class.

R is a comprehensive statistical programming language; if you are going to do the course in R, you should be familiar with that program before starting the course (statistics.com offers several courses in the use of R). S-PLUS is also ideal for resampling, and the instructions for R can also be used for S-PLUS. Stata is another program that provides a good environment for resampling, but help is not available for Stata. Click here for download information for these and other software packages that offer free or nominal cost versions that may be used in statistics.com courses.

You must have a copy of R for the course. Click Here for information on obtaining a free copy.

Register Now

Yes, I want to register for:

Bootstrap Methods

Instructor(s):
Dates:
March 16, 2012 to April 13, 2012
Course Fee: $499
Academic Discounted Rate: $399

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

I am affiliated with an academic institution
I am not affiliated with an academic institution