Bootstrapping
The bootstrap is a computer-based statistical technique that is used to obtain measures of precision of parameter estimates. Although the technique is sufficiently general to be used in time-series analysis, permutation tests, cross-validation, nonlinear regression, and cluster analysis, its most common use is to compute standard errors and confidence intervals. Introduced by Bradley Efron in 1979, the procedure itself belongs in a broader class of estimators that use sampling techniques to create empirical distributions by resampling from the original data set. The goal of the procedure is to produce analytic expressions for estimators that are difficult to calculate mathematically. The name itself derives from the popular story in which Baron von Munchausen (after whom Munchausen syndrome is also named) was stuck at the bottom of ...
Looks like you do not have access to this content.
Reader's Guide
Descriptive Statistics
Distributions
Graphical Displays of Data
Hypothesis Testing
Important Publications
Inferential Statistics
Item Response Theory
Mathematical Concepts
Measurement Concepts
Organizations
Publishing
Qualitative Research
Reliability of Scores
Research Design Concepts
Research Designs
Research Ethics
Research Process
Research Validity Issues
Sampling
Scaling
Software Applications
Statistical Assumptions
Statistical Concepts
Statistical Procedures
Statistical Tests
Theories, Laws, and Principles
Types of Variables
Validity of Scores
- All
- A
- B
- C
- D
- E
- F
- G
- H
- I
- J
- K
- L
- M
- N
- O
- P
- Q
- R
- S
- T
- U
- V
- W
- X
- Y
- Z