The goal of an experiment is to evaluate the profit, loss, or the amount of a physical entity over a period. The measurements $X_t$ can be influenced by the values measured in the past; hence we describe the situation with an autoregression model, whose autoregression coefficients are generally unknown. The variable of interest is the error term $Z_t$ of the model, which is the increment of $X_t$ with respect to the past, but itself unobservable. The problem is to estimate various quantile functions of $Z$, as the risk measure of the loss or the related economic indicators. We construct an estimate of quantile functions of $Z$ in the situation that the inference is possible only by means of observations $X$. The proposed estimates are based on the R-estimators of autoregression coefficients, combined with the autoregression quantiles.

翻译：实验的目标是评估一段时间内的利润、损失或实体数量。测量值$X_t$可能受到过去测量值的影响；因此，我们使用自回归模型描述这一情境，其自回归系数通常是未知的。关注变量是模型的误差项$Z_t$，即$X_t$相对于过去的变化量，但其本身不可观测。问题在于估计$Z$的各种分位数函数，作为损失或相关经济指标的风险度量。我们构建了在仅能通过观测值$X$进行推断的情境下$Z$的分位数函数的估计方法。所提出的估计基于自回归系数的R估计与自回归分位数的结合。