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

Quantile regression can be used to obtain a non-parametric estimate of a conditional quantile function. The presence of quantile crossing, however, leads to an invalid distribution of the response and makes it difficult to use the fitted model for prediction. In this work, we show that crossing can...

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Bibliographic Details
Main Authors: Sottile, Gianluca, Frumento, Paolo
Other Authors: Arbia, G, Peluso, S, Pini, A, Rivellini, G, Sottile, G, Frumento, P
Format: Book Part
Language:English
Published: Pearson 2019
Subjects:
Parametric quantile functions
quantile regression coefficients modelling (QRCM)
R package qrcm
estimation of extremes
climate change.
Settore SECS-S/01 - Statistica
Arctic
Climate change
Online Access:http://hdl.handle.net/10447/419560
https://it.pearson.com/content/dam/region-core/italy/pearson-italy/pdf/Dirigenti e istituzioni/ISTITUZIONI-HE-PDF-sis2019_V4.pdf
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Summary:Quantile regression can be used to obtain a non-parametric estimate of a conditional quantile function. The presence of quantile crossing, however, leads to an invalid distribution of the response and makes it difficult to use the fitted model for prediction. In this work, we show that crossing can be alleviated by modelling the quantile function parametrically. We then describe an algorithm for constrained optimisation that can be used to estimate parametric quantile functions with the noncrossing property. We investigate climate change by modelling the long-term trends of extreme temperatures in the Arctic Circle.

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