Reliable estimates of quantiles associated with mid‐to‐large return periods are required in the everyday practice of Hydrologic Engineering. However, the usually small samples pose numerous challenges for inferring such quantiles. Therefore, augmenting sample sizes via extension techniques could be beneficial for statistical inference. This paper attempts to provide a comprehensive assessment of the performance of a collection of such techniques in estimating rare and extreme quantiles. Regression models, such as the ordinary least squares (OLS) approach and the Generalised Linear Models (GLM), as well as techniques specifically designed for time series extension, such as the Maintenance of Variance (MOVE) family, were evaluated by means of Monte Carlo simulations. Results show that, for both two and three‐parameter distributional models and any level of association, the MOVE3 and MOVE4 techniques appear to provide the best balance between bias and precision of extreme quantile estimates.

中文翻译：

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在极端分位数估计中评估扩展水文时间序列的影响

在水文工程的日常实践中，需要可靠地估计与大中型回报期有关的分位数。但是，通常较小的样本在推论此类分位数方面面临许多挑战。因此，通过扩展技术增加样本量可能对统计推断有益。本文试图对此类技术的集合在评估稀有和极端分位数方面的性能进行全面评估。使用Monte评估了回归模型，例如普通最小二乘（OLS）方法和广义线性模型（GLM），以及专为时间序列扩展而设计的技术，例如方差保持（MOVE）系列。卡洛模拟。结果表明，