@article{HauptFritsch, author = {Haupt, Harry and Fritsch, Markus}, title = {Quantile Trend Regression and Its Application to Central England Temperature}, series = {Mathematics}, volume = {10}, journal = {Mathematics}, number = {3}, editor = {Rykov, Vladimir V.}, publisher = {MDPI}, issn = {2227-7390}, doi = {10.3390/math10030413}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-10382}, abstract = {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.}, language = {en} } @article{FritschHauptSchnurbus2024, author = {Fritsch, Markus and Haupt, Harry and Schnurbus, Joachim}, title = {Efficiency of poll-based multi-period forecasting systems for German state elections}, series = {International Journal of Forecasting (Online ISSN: 1872-8200)}, volume = {41 (2025)}, journal = {International Journal of Forecasting (Online ISSN: 1872-8200)}, number = {2}, publisher = {Elsevier}, address = {Amsterdam}, doi = {10.1016/j.ijforecast.2024.04.008}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-18793}, pages = {670 -- 688}, year = {2024}, abstract = {Election polls are frequently employed to reflect voter sentiment with respect to a particular election (or fixed-event). Despite their widespread use as forecasts and inputs for predictive algorithms, there is substantial uncertainty regarding their efficiency. This uncertainty is amplified by judgment in the form of pollsters applying unpublished weighting schemes to ensure the representativeness of the sampled voters for the underlying population. Efficient forecasting systems incorporate past information instantly, which renders a given fixed-event unpredictable based on past information. This results in all sequential adjustments of the fixed-event forecasts across adjacent time periods (or forecast revisions) being martingale differences. This paper illustrates the theoretical conditions related to weak efficiency of fixed-event forecasting systems based on traditional least squares loss and asymmetrically weighted least absolute deviations (or quantile) loss. Weak efficiency of poll-based multi-period forecasting systems for all German federal state elections since the year 2000 is investigated. The inefficiency of almost all considered forecasting systems is documented and alternative explanations for the findings are discussed.}, language = {en} } @article{BauerHauptLinner2024, author = {Bauer, Ida and Haupt, Harry and Linner, Stefan}, title = {Pinball boosting of regression quantiles}, series = {Computational Statistics \& Data Analysis (Online ISSN: 1872-7352)}, volume = {2024}, journal = {Computational Statistics \& Data Analysis (Online ISSN: 1872-7352)}, number = {200}, publisher = {Elsevier}, address = {Amsterdam}, doi = {10.1016/j.csda.2024.108027}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-18850}, pages = {12 Seiten}, year = {2024}, abstract = {An algorithm for boosting regression quantiles using asymmetric least absolute deviations, better known as pinball loss, is proposed. Existing approaches for boosting regression quantiles are essentially equal to least squares boosting of regression means with the single difference that their working residuals are based on pinball loss. All steps of our boosting algorithm are embedded in the well-established framework of quantile regression, and its main components - sequential base learning, fitting, and updating - are based on consistent scoring rules for regression quantiles. The Monte Carlo simulations performed indicate that the pinball boosting algorithm is competitive with existing approaches for boosting regression quantiles in terms of estimation accuracy and variable selection, and that its application to the study of regression quantiles of hedonic price functions allows the estimation of previously infeasible high-dimensional specifications.}, language = {en} }