Point prediction of future upper record values is considered. For an underlying absolutely continuous distribution with strictly increasing cumulative distribution function, the general form of the predictor obtained by maximizing the observed predictive likelihood function is established. The results are illustrated for the exponential, extreme-value and power-function distributions, and the performance of the obtained predictors is compared to that of maximum likelihood predictors on the basis of the mean squared error and the Pitman’s measure of closeness criteria. For exponential and extreme-value distributions, it is shown that under slight restrictions, the maximum observed likelihood predictor outperforms the maximum likelihood predictor in terms of both performance criteria.

考虑将来的上记录值的点预测。对于具有严格增加的累积分布函数的底层绝对连续分布，建立了通过最大化观察到的预测似然函数而获得的预测变量的一般形式。说明了指数，极值和幂函数分布的结果，并根据均方误差和Pitman贴近度度量标准，将获得的预测变量的性能与最大似然预测变量的性能进行了比较。对于指数分布和极值分布，显示出在两个限制条件下，就两个性能标准而言，观察到的最大似然预测值均优于最大似然预测值。