Prof. Dr. Jürgen Groß

Aktuell (WiSe 25/26):

• Basislernbereich Mathematik: Einführung in die Mathematik
• Vertiefung Schätzen und Testen
• Topics in Statistical Inference 
• Fachwissenschaftliches Seminar 'Themen der Schulmathematik'

Frühere Vorlesungen:

• Grundlagen der Mathematik (WiSe 24/25)
• Geometrie (SoSe 17, SoSe 18, SoSe 19, SoSe 21)
• Statistik und Stochastik (SoSe 17, SoSe 18, SoSe 19, SoSe 20, SoSe 21, SoSe 22, SoSe 23, SoSe 24, SoSe 25)
• Stochastische Prozesse  (WiSe 16/17, WiSe17/18, WiSe 18/19, WiSe 19/20, SoSe 21)
• Analysis (WiSe 19/20, WiSe 20/21, WiSe 22/23, WiSe 23/24, SoSe 24, SoSe 25)
• Vertiefung Lineare Algebra (WiSe 20/21, WiSe 21/22, WiSe 22/23, WiSe 24/25)
• Vertiefung Schätzen und Testen (SoSe 22, SoSe 23)
• Mathematische Methoden II: Lineare Algebra (SoSe 22, SoSe 23)
• Mathematische Methoden III: Analysis (WiSe23/24, WiSe 24/25)
• Topics in Statistical Inference (WiSe 24/25)
• Analytische Methoden (Mathematische Methoden II) (SoSe 20)
• Statistische Methoden (Mathematische Methoden III) (WiSe 16/17, WiSe 17/18, WiSe 18/19)
• Datenanalyse und Statistik (WiSe 16/17, WiSe 17/18, WiSe 18/19, WiSe 19/20, WiSe 21/22, WiSe 23/24)
• Survey Sampling (WiSe 20/21, WiSe 21/22, WiSe 22/23)

Mein Forschungsinteresse liegt im Bereich angewandter und theoretischer statistischer Methoden. Dazu zählen als Schwerpunkte:

• Lineare statistische Modelle und verwandte Matrixalgebra
• Kognitive Diagnosemodelle
• Statistische Nachbereitung von Ensemble-Wetterprognosen

• Jürgen Groß (2025). Ein Produkt von Wahrscheinlichkeiten. Stochastik in der Schule, 45(3), 29-34.
• Jürgen Groß and Annette Möller (2025). Effect Size Estimation in Linear Mixed Models. METRON (2025). doi.org/10.1007/s40300-025-00295-w
• David Jobst, Annette Möller, Jürgen Groß (2025). D-vine Generalized Additive Model copula-based quantile regression with application to ensemble postprocessing, Journal of the Royal Statistical Society Series C: Applied Statistics, 994–1020, 2025; https://doi.org/10.1093/jrsssc/qlaf011 
• David Jobst, Annette Möller and Jürgen Groß (2024b). Gradient-Boosted Generalized Linear Models for Conditional Vine Copulas. Environmetrics, 2024; 35:e2887. Available from: https://doi.org/10.1002/env.2887 [Open Access]
• David Jobst, Annette Möller and Jürgen Groß (2024a). Time Series based Ensemble Model Output Statistics for Temperature Forecasts Postprocessing. Quarterly Journal of the Royal Meteorological Society, 1–18. Available from: https://doi.org/10.1002/qj.4844 [Open Access]
• Jürgen Groß and Annette Möller (2024). Some additional remarks on statistical properties of Cohen's d in the presence of covariates. Statistical Papers. DOI: https://doi.org/10.1007/s00362-023-01527-9 [Open Access]
• David Jobst, Annette Möller and Jürgen Groß (2023). D-Vine Copula based Postprocessing of Wind Speed Ensemble Forecasts. Quarterly Journal of the Royal Meteorological Society, 149(755),  2575–2597. DOI: https://doi.org/10.1002/qj.4521
• Jürgen Groß and Annette Möller (2023). A Note on Cohen's d From a Partitioned Linear Regression Model. Journal of Statistical Theory and Practice 17, 22. DOI: https://doi.org/10.1007/s42519-023-00323-w [Open Access]
• Annette Möller, Ann Cathrice George and Jürgen Groß (2022). Predicting school transition rates in Austria with classification trees. International Journal of Research & Method in Eduction, DOI: 10.1080/1743727X.2022.2128744 [arXiv:2210.11580]
• Sebastian Lerch, Sándor Baran, Annette Möller, Jürgen Groß, Roman Schefzik, Stephan Hemri and Maximiliane Graeter (2020). Simulation-based comparison of multivariate ensemble post-processing methods.  Nonlinear Processes in Geophysics, 27, 349–371. https://doi.org/10.5194/npg-27-349-2020 [Open Access]
• Annette Möller and Jürgen Groß (2020). Probabilistic temperature forecasting with a heteroscedastic autoregressive ensemble postprocessing model. Quaterly Journal of the Royal Meteorological Society, 146, 211–224. https://doi.org/10.1002/qj.3667 [Open Access]

Preprints

• Jürgen Groß and Annette Möller (2024). The zero degree of freedom non-central chi squared distribution for ensemble postprocessing. arXiv:2404.04964
• Jürgen Groß (2021). A Note On The Randomized Kaczmarz Method With A Partially Weighted Selection Step. arXiv:2105.14583

• Research Gate: researchgate.net/profile/Juergen_Gross2
• Google Scholar: https://scholar.google.de/citations?hl=en&user=vBWC0XIAAAAJ

Institut für Mathematik, Mathematikdidaktik und Informatikdidaktik
Samelsonplatz 1
31141 Hildesheim

Sekretariat:
Telefon: +49 (0) 5121 - 883 40100
Telefax: +49 (0) 5121 - 883 40101
E-Mail: institutimmi@uni-hildesheim.de

Öffnungszeiten:
Mo. bis Do. 09.00 - 12:00 Uhr
Fr. 09 - 11.00 Uhr (im Semester)

Zudem telefonisch erreichbar:
Mo. - Do. 8:00 - 15.30 Uhr
Fr. 8:00 - 11.00 Uhr

Mediothek:

Öffnungszeiten:
Mo. 10.00 - 12:00 Uhr und 14.00 - 17.00 Uhr
Di. 8:00 - 13:00 Uhr