First approximate calibration results for discrete properties in multiclass settings via Lipschitz intermediaries for strongly orderable discrete properties.
Electronic Journal of Statistics 17(2): 3226--3286
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Kling-Gupta linear regression scales the OLS coefficient vector by a variance-inflation factor based on sample moments, preserves response variance on the training set, and converges almost surely to explicit population limits while maximizing KGE but not NSE.
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Smoothed Elicitation Complexity for Approximate $\Gamma$-calibration of Discrete Classification Tasks
First approximate calibration results for discrete properties in multiclass settings via Lipschitz intermediaries for strongly orderable discrete properties.
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Kling-Gupta linear regression
Kling-Gupta linear regression scales the OLS coefficient vector by a variance-inflation factor based on sample moments, preserves response variance on the training set, and converges almost surely to explicit population limits while maximizing KGE but not NSE.