Jackknife bias and standard error
Definition taught in Estimating error rates, part of Statistical learning
Jackknife bias and standard error
| Type | Definition |
| Taught in | Estimating error rates |
| Part | Statistical learning |
\widehat{\mathrm{bias}}_{\mathrm{jack}} =(n-1)\bigl(\hat\theta_{(\cdot)}-\hat\theta\bigr), \qquad \widehat{\mathrm{se}}_{\mathrm{jack}} =\Biggl(\frac{n-1}{n}\sum_{i=1}^n\bigl(\hat\theta_{(i)}-\hat\theta_{(\cdot)}\bigr)^2\Biggr)^{1/2}.
The bias-corrected estimate is \hat\theta-\widehat{\mathrm{bias}}_{\mathrm{jack}}, which rearranges to n\hat\theta-(n-1)\hat\theta_{(\cdot)}.
Used in
- Taught in Estimating error rates, where the derivation and examples are.
- All 7 questions on Estimating error rates
See also
Fixed-rule error · Confusion matrix · Apparent error · Leave-one-out cross-validation · -fold cross-validation · Bootstrap error and the correction · A held-out test set · Cross-validation