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Article

Quantile Trend Regression and Its Application to Central England Temperature

Chair of Statistics and Data Analytics, School of Business, Economics and Information Systems, University of Passau, 94032 Passau, Germany
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Author to whom correspondence should be addressed.
Academic Editor: Vladimir V. Rykov
Mathematics 2022, 10(3), 413; https://doi.org/10.3390/math10030413
Received: 30 December 2021 / Revised: 21 January 2022 / Accepted: 25 January 2022 / Published: 28 January 2022
(This article belongs to the Special Issue Statistical Modelling of Complex Environmental Time Series)
The identification and estimation of trends in hydroclimatic time series remains an important task in applied climate research. The statistical challenge arises from the inherent nonlinearity, complex dependence structure, heterogeneity and resulting non-standard distributions of the underlying time series. Quantile regressions are considered an important modeling technique for such analyses because of their rich interpretation and their broad insensitivity to extreme distributions. This paper provides an asymptotic justification of quantile trend regression in terms of unknown heterogeneity and dependence structure and the corresponding interpretation. An empirical application sheds light on the relevance of quantile regression modeling for analyzing monthly Central England temperature anomalies and illustrates their various heterogenous trends. Our results suggest the presence of heterogeneities across the considered seasonal cycle and an increase in the relative frequency of observing unusually high temperatures. View Full-Text
Keywords: temperature; trend modeling; seasonality; heterogeneity; quantile regression temperature; trend modeling; seasonality; heterogeneity; quantile regression
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  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.5884560
    Link: https://github.com/markusfritsch/quantWarming
    Description: Under https://github.com/markusfritsch/quantWarming interested readers can find all data and R code enabling reproducibility of the results in the paper.
MDPI and ACS Style

Haupt, H.; Fritsch, M. Quantile Trend Regression and Its Application to Central England Temperature. Mathematics 2022, 10, 413. https://doi.org/10.3390/math10030413

AMA Style

Haupt H, Fritsch M. Quantile Trend Regression and Its Application to Central England Temperature. Mathematics. 2022; 10(3):413. https://doi.org/10.3390/math10030413

Chicago/Turabian Style

Haupt, Harry, and Markus Fritsch. 2022. "Quantile Trend Regression and Its Application to Central England Temperature" Mathematics 10, no. 3: 413. https://doi.org/10.3390/math10030413

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