Survival regression with proper scoring rules and monotonic neural networks

Survival regression with proper scoring rules and monotonic neural networks

We consider frequently used scoring rules for right-censored survival regression models such as time-dependent concordance, survival-CRPS, integrated Brier score and integrated binomial log-likelihood, and prove that neither of them is a proper scoring rule. This means that the true survival distrib...

Bibliográfalaš dieđut
Váldodahkkit:	Rindt, D, Hu, R, Steinsaltz, D, Sejdinovic, D
Materiálatiipa:	Conference item
Giella:	English
Almmustuhtton:	Journal of Machine Learning Research 2022

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Dahkki: Palade, V, et al.
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