Sarah Brockhaus

Munich University of Applied Sciences — Germany

Mathematics · Physical Sciences

8h-index194citations22works2.02yr avg
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Research Topics

Statistical Methods and Inference(10), Statistical Methods and Bayesian Inference(3), Metabolomics and Mass Spectrometry Studies(3), Advanced Statistical Methods and Models(3), Bayesian Methods and Mixture Models(3)

Publications22 total

Errors-in-variables model fitting for partially unpaired data utilizing mixture models
Statistics·2025· 2 citedOpen Access
Errors-In-Variables Model Fitting for Partially Unpaired Data Utilizing Mixture Models
arXiv (Cornell University)·2024Open Access
Categorization of continuous covariates and complex regression models—friends or foes in intersectionality research
Journal of Clinical Epidemiology·2024· 4 cited
Categorization of continuous covariates and complex regression models – friends or foes in intersectionality research
Research Square (Research Square)·2023Open Access
Boosting Functional Regression Models [R package FDboost version 1.0-0]
2020
PascalKieslich/mousetrap: mousetrap 3.1.5
Zenodo (CERN European Organization for Nuclear Research)·2020Open Access
Boosting functional response models for location, scale and shape with an application to bacterial competition
Statistical Modelling·2020· 1 citedOpen Access
Boosting Functional Regression Models with <b>FDboost</b>
Journal of Statistical Software·2020· 29 citedOpen Access
Boosting functional response models for location, scale and shape with an application to bacterial competition
Open access LMU (Ludwid Maxmilian's Universitat Munchen)·2020· 5 citedOpen Access
Learning from Mouse Movements: Improving Questionnaires and Respondents' User Experience Through Passive Data Collection
2019· 11 cited
Boosting Functional Response Models for Location, Scale and Shape with\n an Application to Bacterial Competition
arXiv (Cornell University)·2018Open Access
Signal Regression Models for Location, Scale and Shape with an Application to Stock Returns
Journal of the Royal Statistical Society Series C (Applied Statistics)·2017· 3 citedOpen Access
Boosting Factor-Specific Functional Historical Models for the Detection of Synchronization in Bioelectrical Signals
Journal of the Royal Statistical Society Series C (Applied Statistics)·2017· 23 cited
Boosting Functional Regression Models with FDboost
arXiv (Cornell University)·2017· 11 citedOpen Access
Learning from mouse movements: Improving questionnaire and respondents' user experience through passive data collection
Econstor (Econstor)·2017· 1 citedOpen Access
Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals
arXiv (Cornell University)·2016Open Access
Boosting flexible functional regression models with a high number of functional historical effects
Statistics and Computing·2016· 32 cited
Signal Regression Models for Location, Scale and Shape with an\n Application to Stock Returns
arXiv (Cornell University)·2016Open Access
Severity of disability related to road traffic crashes in the Spanish adult population
Accident Analysis & Prevention·2016· 12 cited
Boosting functional regression models
Electronic Theses of LMU Munich (Ludwig-Maximilians-Universität München)·2016Open Access
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Frequent Co-authors

Sonja Greven(11), David Rügamer(4), Felix Henninger(3), Pascal J. Kieslich(3), Almond Stöcker(3), Sophia Anna Schaffer(3), Benedikt von Bronk(3), Madeleine Opitz(3), Kornelia Gentsch(2), Klaus R. Scherer(2), Rachel Horwitz(2), Malte Schierholz(2), Florian Keusch(2), Frauke Kreuter(2), David Ruegamer(2), Almond Stoecker(2), Adrian Richter(2), Sabina Ulbricht(2), Andreas Fuest(2), Andreas Mayr(2)