Non-crossing parametric quantile functions: an application to extreme temperatures

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Quantile regression can be used to obtain a non-parametric estimate of aconditional quantile function. The presence of quantile crossing, however, leads toan invalid distribution of the response and makes it difficult to use the fitted modelfor prediction. In this work, we show that crossing can be alleviated by modellingthe quantile function parametrically. We then describe an algorithm for constrainedoptimisation that can be used to estimate parametric quantile functions with the noncrossingproperty. We investigate climate change by modelling the long-term trendsof extreme temperatures in the Arctic Circle.
Original languageEnglish
Title of host publicationSmart Statistics for Smart Applications - Book of Short Papers SIS2019
Number of pages8
Publication statusPublished - 2019


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