Dr. Benjamin Säfken
- Computational Statistics
- Semiparametric Regression
- Model Selection
- Unstructured Distributional Regression
- Natural Language Processing & Topic Modelling
You can find a few topics related to my research interests for master theses here
Current Projects- I'm head of the newly established junior research group "Computational Statistics and Data Science" at the Georg August University of Göttingen
- In the summer term (SS21) I am (stand-in) professor for "Spatial Data Science and Statistical Learning" at the Georg August University of Göttingen
- Verteilungsregression mit künstlichen neuronalen Netzen, funded by the DFG
- Data Science in Economics, funded by the Campus-Institut Data Science
- In the winter term (WS19/20) I am (stand-in) professor at the Ludwig-Maximilians-University in Munich for "Statistics and Data Science"
- Deep Learning Algorithmen – Methoden, Entwicklungen und gesellschaftliche Konsequenzen, funded by Campus QPlus
- Daten Lesen Lernen, funded by Siemens-Nixdorf Stiftung und Deutscher Stifterverband
- Connecting 4 Data Literacy, funded by MWK, Innovation Plus project
Publications
- René Kruse, Alexander Silbersdorff & Benjamin Säfken (2021): Model Averaging for Linear Mixed Models via Augmented Lagrangian, Computational Statistics and Data Analysis (accepted)
- Benjamin Säfken, David Rügamer, Thomas Kneib, and Sonja Greven (2021): Conditional Model Selection in Mixed-Effects Models with cAIC4, Journal of Statistical Software
- Thomas Kneib, Alexander Silbersdorff & Benjamin Säfken (2021): Rage Against the Mean – A Review of Distributional Regression Approaches, Econometrics and Statistics
- Anton Thielmann, Christoph Weisser, Astrid Krenz & Benjamin Säfken (2021): Unsupervised Document Classification integrating Web Scraping, One-Class SVM and LDA Topic Modelling, Journal of Applied Statistics
- Colin Griesbach, Benjamin Säfken & Elisabeth Waldmann (2021): Gradient Boosting for Linear Mixed Models, The International Journal of Biostatistics
- Jana Lasser, Debshanka Manik, Alexander Silbersdorff, Benjamin Säfken & Thomas Kneib (2020): Introductory data science across disciplines, using Python, case studies and industry consulting projects, Teaching Statistics
- Gillian Kant, Christoph Weisser & Benjamin Säfken (2020): TTLocVis: A Twitter Topic Location Visualization Package. Journal of Open Source Software, 5 (54), 2507
- Benjamin Säfken and Thomas Kneib (2020): Conditional Covariance Penalties for Mixed Models, Scandinavian Journal of Statistics; 47: 990– 1010
- Simon N. Wood, Natalya Pya & Benjamin Säfken (2016): Smoothing parameter and model selection for general smooth models, Journal of the American Statistical Association, Vol. 111 , Iss. 516, 2016
- Benjamin Säfken (2015): Model choice and variable selection in mixed & semiparametric models , Dissertation - Georg-August-Universität Göttingen
- Benjamin Säfken (2015): Semiparametrische Regressionsmodelle in der Versorgungsplanung, Springer Spektrum ISBN 978-3-658-08786-9
- Benjamin Säfken, Thomas Kneib, Clara-Sophie van Waveren & Sonja Greven (2014): A unifying approach to the estimation of the conditional Akaike information in generalized linear mixed models, Electronic Journal of Statistics, 8,201-225. 2014
- Benjamin Säfken; Sonja Greven & Thomas Kneib (2013): Estimating prediction error in mixed models, Proceedings of the 28th International Workshop on Statistical Modelling, 2013
- Benjamin Säfken, Martin Rohde, Mathias Mertens, Rolf Annuß, Hans-Jürgen Appelrath, Thomas Kneib (2012): Fallzahlprognosen in der Versorgungsforschung, Deutsche medizinische Wochenzeitschrift 2012;3) - A287
Teaching
- Statistical and Deep Learning (WiSe 2021/22)
- Statistisches Praktikum (WiSe 2021/22)
- Multivariate Statistics (SoSe 2021)
- Data Science II (SoSe 2021)
- Daten Lesen Lernen (SoSe 2021)
- Statistisches Praktikum (WiSe 2020/21)
- Deep Learning Seminar (WiSe 2020/21)
- Daten Lesen Lernen (SoSe 2020)
- Statistisches Praktikum (SoSe 2020)
- LMU: Mathematische Grundlagen für Nebenfachstudierende (WiSe 2019/20)
- LMU: Seminar zu gemischten und semiparametrischen Modellen (WiSe 2019/20)
- LMU: Statistik III (WiSe 2019/20)
- LMU: Statistisches Praktikum (WiSe 2019/20)
- Deep Learning Seminar (WiSe 2019/20)
- Summer School Data Science (SoSe 2019)
- Daten Lesen Lernen (SoSe 2019)
- Graduate Seminar in Statistics and Econometrics (SoSe 2019)
- Statistisches Praktikum (SoSe 2019)
- Deep Learning Seminar (WiSe 2018/19)
- Fortgeschrittene Mathematik (WiSe 2018/19)
- Graduate Seminar in Statistics and Econometrics (WiSe 2018/19)
- Statistisches Praktikum (WiSe 2018/19)
- Summer School Data Science (SoSe 2018)
- Graduate Seminar in Statistics and Econometrics (SoSe 2018)
- Statistisches Praktikum (SoSe 2018)