Abstract
AbstractReliable 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
