Metadata
- Author: Tim Sumner
- Full Title:: Stein’s Paradox
- Category:: 🗞️Articles
- Document Tags:: Statistics,
- URL:: https://substack.com/redirect/4a26b00e-429b-499f-8d64-ca9c145cc7d0?j=eyJ1IjoiNDRpMmEifQ.txKr3BEB06jM7pp-5wphmyXof7jFdPvpfRX5kIjhK8g
- Finished date:: 2024-10-11
Highlights
The idea behind the James-Stein estimator is to “shrink” individual sample means toward a central value (the grand mean), which reduces the overall estimation error. (View Highlight)
The general formula[3] for the James-Stein estimator is:
Where: • x is the sample mean vector. • μ is the grand mean (the average of the sample means). • c is a shrinkage factor that lies between 0 and 1. It determines how much we pull the individual means toward the grand mean. (View Highlight)