Prof. Dr. Sonja Greven

Profil

Zusammenfassung

Prof. Greven entwickelt flexible statistische Methoden für komplexe Datenstrukturen, insbesondere für funktionale Daten (Kurven, Formen, Bilder) und wiederholte Messungen. Ihre Expertise liegt in der Kombination von Regressionsmethoden, gemischten Modellen und Dimensionsreduktion, um strukturierte biomedizinische und epidemiologische Daten zu analysieren. Sie verbindet dabei klassische Statistik mit modernen Ansätzen wie Deep Learning und entwickelt Werkzeuge für praktische Anwendungen von der Datenqualitätskontrolle bis zur Langzeiteffektschätzung.

Skills

Name

Prof. Dr. Sonja Greven

Titel

Prof. Dr.

Fakultät

Wirtschaftswissenschaftliche Fakultät

Institut

Dekan(in) / Prodekan(in) Dekanat

Arbeitsgruppe

Prodekan(in) für Studium, Lehre und Internationales

E-Mail

🔒 nur für eingeloggte sichtbarAnmelden

Telefon

🔒 nur für eingeloggte sichtbarAnmelden

HU-FIS-Profil

Quelle ↗

Zuletzt gescrapt

27.6.2026, 01:06:36

• Flexible Dichteregressionsmethoden

  Quelle ↗

  Förderer: DFG Sachbeihilfe Zeitraum: 10/2023 - 09/2026 Projektleitung: Prof. Dr. Sonja Greven

• Flexible Regressionsmethoden für Kurven and Formen

  Quelle ↗

  Förderer: DFG Sachbeihilfe Zeitraum: 03/2026 - 03/2029 Projektleitung: Prof. Dr. Sonja Greven

• Flexible Regressionsmethoden für Kurven und Formen

  Quelle ↗

  Förderer: DFG Sachbeihilfe Zeitraum: 01/2020 - 01/2023 Projektleitung: Prof. Dr. Sonja Greven

🔒 Das System hat 280 mögliche Industrie-Partner gefunden — Firmen, Scores und Begründungen sind nur für eingeloggte Nutzer:innen sichtbar. Anmelden

• Cardiovascular Events during World Cup Soccer

  2008

  New England Journal of Medicine · 428 Zitationen · DOI

  BACKGROUND: The Fédération Internationale de Football Association (FIFA) World Cup, held in Germany from June 9 to July 9, 2006, provided an opportunity to examine the relation between emotional stress and the incidence of cardiovascular events. METHODS: Cardiovascular events occurring in patients in the greater Munich area were prospectively assessed by emergency physicians during the World Cup. We compared those events with events that occurred during the control period: May 1 to June 8 and July 10 to July 31, 2006, and May 1 to July 31 in 2003 and 2005. RESULTS: Acute cardiovascular events were assessed in 4279 patients. On days of matches involving the German team, the incidence of cardiac emergencies was 2.66 times that during the control period (95% confidence interval [CI], 2.33 to 3.04; P<0.001); for men, the incidence was 3.26 times that during the control period (95% CI, 2.78 to 3.84; P<0.001), and for women, it was 1.82 times that during the control period (95% CI, 1.44 to 2.31; P<0.001). Among patients with coronary events on days when the German team played, the proportion with known coronary heart disease was 47.0%, as compared with 29.1% of patients with events during the control period. On those days, the highest average incidence of events was observed during the first 2 hours after the beginning of each match. A subanalysis of serious events during that period, as compared with the control period, showed an increase in the incidence of myocardial infarction with ST-segment elevation by a factor of 2.49 (95% CI, 1.47 to 4.23), of myocardial infarction without ST-segment elevation or unstable angina by a factor of 2.61 (95% CI, 2.22 to 3.08), and of cardiac arrhythmia causing major symptoms by a factor of 3.07 (95% CI, 2.32 to 4.06) (P<0.001 for all comparisons). CONCLUSIONS: Viewing a stressful soccer match more than doubles the risk of an acute cardiovascular event. In view of this excess risk, particularly in men with known coronary heart disease, preventive measures are urgently needed.

• Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains

  2017

  Journal of the American Statistical Association · 349 Zitationen · DOI

  Existing approaches for multivariate functional principal component analysis are restricted to data on the same one-dimensional interval. The presented approach focuses on multivariate functional data on different domains that may differ in dimension, such as functions and images. The theoretical basis for multivariate functional principal component analysis is given in terms of a Karhunen–Loève Theorem. For the practically relevant case of a finite Karhunen–Loève representation, a relationship between univariate and multivariate functional principal component analysis is established. This offers an estimation strategy to calculate multivariate functional principal components and scores based on their univariate counterparts. For the resulting estimators, asymptotic results are derived. The approach can be extended to finite univariate expansions in general, not necessarily orthonormal bases. It is also applicable for sparse functional data or data with measurement error. A flexible R implementation is available on CRAN. The new method is shown to be competitive to existing approaches for data observed on a common one-dimensional domain. The motivating application is a neuroimaging study, where the goal is to explore how longitudinal trajectories of a neuropsychological test score covary with FDG-PET brain scans at baseline. Supplementary material, including detailed proofs, additional simulation results, and software is available online.

• Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models

  2007

  Computational Statistics & Data Analysis · 332 Zitationen · DOI

• Eberhard Karls Universität Tübingen

  FOR 5363: KI-FOR Integration von Deep Learning und Statistik zum Verständnis strukturierter biomedizinischer Daten

  university

• Hasso-Plattner-Institut für Digital Engineering

  FOR 5363: KI-FOR Integration von Deep Learning und Statistik zum Verständnis strukturierter biomedizinischer Daten

  research_institute

• Karlsruher Institut für Technologie

  FOR 5363: KI-FOR Integration von Deep Learning und Statistik zum Verständnis strukturierter biomedizinischer Daten

  university