TY - JOUR A1 - Haupt, Harry A1 - Fritsch, Markus A2 - Rykov, Vladimir V. T1 - Quantile Trend Regression and Its Application to Central England Temperature T2 - Mathematics N2 - 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. KW - temperature KW - trend modeling KW - seasonality KW - heterogeneity KW - quantile regression KW - C02 KW - C14 KW - C18 KW - C22 KW - Q54 Y1 - 2022 UR - https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/1038 UR - https://nbn-resolving.org/urn:nbn:de:bvb:739-opus4-10382 SN - 2227-7390 VL - 10 IS - 3 PB - MDPI ER -