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.
Hydrological Sciences Journal 70(8): 1248--1259
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Musielak-Orlicz spaces enable bounding cumulants in uncertain supOU long-memory processes by state-dependent divergences on reversion and Levy measures, succeeding where Kullback-Leibler fails.
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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.
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A Musielak-Orlicz approach for modeling uncertainties in long-memory processes
Musielak-Orlicz spaces enable bounding cumulants in uncertain supOU long-memory processes by state-dependent divergences on reversion and Levy measures, succeeding where Kullback-Leibler fails.