Jackknife+ Leave-One-Out Intervals
Combine leave-one-out fits and residuals with the finite-sample Jackknife+ quantile correction.
Free theory articleCombine leave-one-out fits and residuals with the finite-sample Jackknife+ quantile correction.
Concept & interpretation
Jackknife+ is a predictive inference method that combines leave-one-out cross-validation residuals with finite-sample quantile corrections. It provides distribution-free marginal coverage guarantees by evaluating the model's stability when excluding one training data point at a time.
Interactive Experiment
Use the Jackknife+ Leave-One-Out Intervals module to explore how the interval changes.
- Set "Data" to "Outlier".
- Set "Training size" to 6.
- Set "Alpha" to 0.25.
- Set "Test x" to 0.
- Observe the calculated "Lower order index" is 1 and "Upper order index" is 6.
- The resulting "Jackknife lower" is -3.0 and "Jackknife upper" is 5.0.
- Change "Alpha" to 0.1 and "Training size" to 4. Notice that the bounds become -Infinity and Infinity.