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.

Sottile, G., Frumento, P. (2019). Non-crossing parametric quantile functions: an application to extreme temperatures. In G. Arbia, S. Peluso, A. Pini, G. Rivellini (a cura di), Smart Statistics for Smart Applications - Book of Short Papers SIS2019 (pp. 533-540). Milano : Pearson.

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

Sottile, Gianluca
;
2019-01-01

Abstract

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.
Settore SECS-S/01 - Statistica
9788891915108
https://it.pearson.com/content/dam/region-core/italy/pearson-italy/pdf/Dirigenti e istituzioni/ISTITUZIONI-HE-PDF-sis2019_V4.pdf
Sottile, G., Frumento, P. (2019). Non-crossing parametric quantile functions: an application to extreme temperatures. In G. Arbia, S. Peluso, A. Pini, G. Rivellini (a cura di), Smart Statistics for Smart Applications - Book of Short Papers SIS2019 (pp. 533-540). Milano : Pearson.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/419560
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