Scoring rules are used to evaluate the quality of predictions that take the form of probability distributions. A scoring rule is strictly proper if its expected value is uniquely minimized by the true probability distribution. One of the most well-known and widely used strictly proper scoring rules is the logarithmic scoring rule. We propose a version of the logarithmic scoring rule for competing risks data and show that it remains strictly proper under non-informative censoring.

翻译：分级规则用于评估以概率分布为形式的预测质量。如果其预期值被真实概率分布所独有地最小化,那么评分规则是完全正确的。最广为人知和广泛使用的评分规则之一是对数评分规则。我们为相互竞争的风险数据提出了一个对数评分规则版本,并表明在非信息化审查下,评分规则仍然严格正确。