Prediction of quantiles at extreme tails is of interest in numerous applications. Extreme value modelling provides various competing predictors for this point prediction problem. A common method of assessment of a set of competing predictors is to evaluate their predictive performance in a given situation. However, due to the extreme nature of this inference problem, it can be possible that the predicted quantiles are not seen in the historical records, particularly when the sample size is small. This situation poses a problem to the validation of the prediction with its realisation. In this article, we propose two non-parametric scoring approaches to assess extreme quantile prediction mechanisms. The proposed assessment methods are based on predicting a sequence of equally extreme quantiles on different parts of the data. We then use the quantile scoring function to evaluate the competing predictors. The performance of the scoring methods is compared with the conventional scoring method and the superiority of the former methods are demonstrated in a simulation study. The methods are then applied to reanalyse cyber Netflow data from Los Alamos National Laboratory and daily precipitation data at a station in California available from Global Historical Climatology Network.

翻译：极端尾矿中的孔径预测是许多应用中感兴趣的。极端价值建模为这一点预测问题提供了各种相互竞争的预测数据。一套相互竞争的预测数据的共同评估方法是在特定情况下评价其预测性能。然而,由于这一推论问题的极端性质,在历史记录中可能看不到预测的孔径,特别是当样本大小很小时。这种情况对实现预测的验证造成问题。在本条中,我们提出了两种非参数评分方法来评估极端孔径预测机制。提议的评估方法以预测数据不同部分的同样极端的孔径序列为基础。我们然后使用定量评分功能来评价相互竞争的预测数据。评分方法的性能与常规评分方法相比较,以前方法的优越性在模拟研究中得到证明。然后将这种方法用于重新分析来自Los Alamos国家实验室的网络流数据和来自全球历史气候学网络加利福尼亚州一个站的每日降水量数据。