Strictly Proper Scoring Rules, Prediction, and Estimation¶
Gneiting, T., & Raftery, A. E. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association, 102(477), 359-378.
Cited by¶
5 citations across 5 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Calibration
- This decomposition breaks what might otherwise seem like an overwhelming problem—"make this system accurate"—into discrete, manageable steps, paralleling the way Gneiting and Raftery (2007) decompose strictly proper scoring rules into reliability and resolution components.
This sourceModern theory of proper scoring rules on general probability spaces; rules are proper when honest probabilistic forecasts maximize expected score, supporting principled measurement of calibration (reliability) and resolution.
- This decomposition breaks what might otherwise seem like an overwhelming problem—"make this system accurate"—into discrete, manageable steps, paralleling the way Gneiting and Raftery (2007) decompose strictly proper scoring rules into reliability and resolution components.
Domain-specific¶
Mechanisms¶
- Confidence Update Log
- Because entries carry probabilities, they can be scored against outcomes with a proper rule such as the Brier score, which is powerful for honest self-assessment.
This sourceEstablishes the Brier score as a proper way to evaluate probabilistic forecasts and encourage honest probability assessments.
- Because entries carry probabilities, they can be scored against outcomes with a proper rule such as the Brier score, which is powerful for honest self-assessment.
- Proper Scoring Rule Comparison
- And a proper score is relative, not absolute: it ranks models against each other, but the winner of a bad field is still bad.
This sourceUses scoring rules to measure forecast quality and rank competing forecast procedures.
- And a proper score is relative, not absolute: it ranks models against each other, but the winner of a bad field is still bad.
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