Shrinkage
Shrinkage reflects the bias found between sample statistics and inferred population parameters. Multiple regression generally overestimates population values from sample multiple correlation coefficients (R) and coefficients of multiple determination (R2). A common adjustment method for overinflation is to use the shrunken or adjusted R2. The adjusted R2 accounts for the amount of shrinkage between the sample R2 and the population squared multiple correlation (ρ2). Similarly, results from a model fitted in one sample are often an overestimate of how it would fit using a separate sample from the same population (i.e., a cross-validation sample), and such results also often need to be adjusted for shrinkage.
This entry begins by explaining why regression overestimates the population parameters. Next, the entry provides an example of shrinkage and discusses ...
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