TY - GEN

T1 - Non-crossing parametric quantile functions: an application to extreme temperatures

AU - Sottile, Gianluca

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10447/419560

UR - https://it.pearson.com/content/dam/region-core/italy/pearson-italy/pdf/Dirigenti e istituzioni/ISTITUZIONI-HE-PDF-sis2019_V4.pdf

M3 - Conference contribution

SN - 9788891915108

SP - 533

EP - 540

BT - Smart Statistics for Smart Applications - Book of Short Papers SIS2019

ER -