Sarah Brockhaus

Research fellow in the working group Biostatistics of Prof. Dr. Sonja Greven
from September 2012 to May 2017.

Contact: Sarah.Brockhaus[at]stat.uni-muenchen.de

Publications

• Brockhaus S, Melcher M, Leisch F and Greven S (2017): Boosting flexible functional regression models with a high number of functional historical effects. Statistics and Computing, 27, 913-926.
• Palmera-Suárez R, López-Cuadrado T, Brockhaus S, Fernández-Cuenca R, Alcalde-Cabero E and Galán I (2016): Severity of disability related to road traffic crashes in the Spanish adult population.
  Accident Analysis & Prevention, 91, 36-42.
• Brockhaus S, Scheipl F, Hothorn T and Greven S (2015): The functional linear array model.
  Statistical Modelling, 15(3), 279-300. Online supplement
• Maierhofer S, Almazan-Isla J, Alcalde-Cabero E and de Pedro-Cuesta J (2011): Prevalence and features of ICF-disability in Spain as captured by the 2008 National Disability Survey. BMC Public Health 2011, 11:897.

Papers in Proceedings

• Brockhaus S, Fuest A, Mayr A and Greven S (2015): Functional regression models for location, scale and shape applied to stock returns. In Friedl H and Wagner H, (eds), Proceedings of the 30th International Workshop on Statistical Modelling, 117–122.
• Brockhaus S, Scheipl F, Hothorn T, Greven S (2014): The functional linear array model and an application to viscosity curves. In Kneib T, Sobotka F, Fahrenholz J and Irmer H, (eds), Proceedings of the 29th International Workshop on Statistical Modelling, 63–68.

Talks

• Boosting historical functional models with an application to fermentation data. At DAGStat 2016, Göttingen, Germany, March 14-18.
• Functional regression models for location, scale and shape applied to stock returns. At 30th IWSM 2015, Linz, Austria, July 6-10.
• The functional linear array model estimated by boosting. At COMPSTAT 2014, Genf, Switzerland, August 19-22.
• The functional linear array model and an application to viscosity curves. At 29th IWSM 2014, Göttingen, Germany, July 14-18.
• Boosting functional regression models. At DStatG-Nachwuchsworkshop 2013, September 16-17, Berlin, Germany.

Current Preprints / Technical Reports / Submitted

• Brockhaus S, Rügamer D and Greven S (2017): Boosting Functional Regression Models with FDboost.
• Brockhaus S, Fuest A, Mayr A and Greven S (2016): Signal regression models for location, scale and shape with an application to stock returns. arXiv.
• Rügamer D, Brockhaus S, Gentsch C, Scherer K and Greven S (2016): Boosting factor-specific functional historical models for the detection of synchronisation in bioelectrical signals. arXiv.
• Horwitz R, Brockhaus S, Henninger F, Keusch F, Kieslich PJ, Kreuter F and Schierholz M (2016): Learning from Mouse Movements: Improving Questionnaire and Respondents’ User Experience through Passive Data Collection.

Software

• Brockhaus S and Rügamer D (2017): FDboost: Boosting Functional Regression Models, R package,
  on github, on CRAN

Theses

• PhD thesis: Boosting functional regression models
• Master thesis: Flexible Regressionsmodelle für Zähldaten
• Bachelor thesis: Erweiterung von CHAID auf metrische Zielgrößen

Teaching

Semester	Lehrveranstaltung
WiSe 2016/17	Vorlesung Statistik III für Nebenfachstudierende
SoSe 2016	Übung zu Wahrscheinlichkeitstheorie und Inferenz II
WiSe 2015/16	Übung zu Schätzen und Testen I
SoSe 2015	Übung zu Gemischte Modelle
WiSe 2014/15	Seminar Funktionale Datenanalyse
WiSe 2014/15	Übung zu Schätzen und Testen I
SoSe 2014	Übung zu Statistik II
SoSe 2013	Übung zu Gemischte Modelle
WiSe 2012/13	Statistisches Praktikum in den Semesterferien
WiSe 2012/13	Seminar Funktionale Datenanalyse

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