Distribution-Free Predictive Inference for Regression¶
Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., & Wasserman, L. (2018). Distribution-Free Predictive Inference for Regression. Journal of the American Statistical Association, 113(523), 1094-1111.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- Calibration-Set Interval Adjustment
- Its strength is that it delivers valid finite-sample coverage with almost no assumption about the model or the error distribution — as long as the calibration data are exchangeable with deployment data, the guarantee is essentially model-free, and the same held-out machinery naturally extends into a monitoring loop.
This sourceConformal prediction gives finite-sample marginal coverage under exchangeability without specifying a regression-error distribution.
- Its strength is that it delivers valid finite-sample coverage with almost no assumption about the model or the error distribution — as long as the calibration data are exchangeable with deployment data, the guarantee is essentially model-free, and the same held-out machinery naturally extends into a monitoring loop.
Verification¶
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Does it back the claim? Not recorded. The single citation of this work carries no recorded support check.
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Registry ID ref:5b88f296aa1e · see in the full table