Benjamin Säfken
Benjamin Säfken
Data Science & Statistics, Institute of Mathematics, Clausthal University of Technology
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Zitiert von
Zitiert von
Smoothing parameter and model selection for general smooth models
SN Wood, N Pya, B Säfken
Journal of the American Statistical Association 111 (516), 1548-1563, 2016
Conditional Model Selection in Mixed-Effects Models with cAIC4
B Säfken, D Rügamer, T Kneib, S Greven
Journal of Statistical Software 99 (8), 2021
A unifying approach to the estimation of the conditional Akaike information in generalized linear mixed models
B Saefken, T Kneib, CS van Waveren, S Greven
Rage against the mean–a review of distributional regression approaches
T Kneib, A Silbersdorff, B Säfken
Econometrics and Statistics, 2021
cAIC4: Conditional Akaike information criterion for lme4
B Saefken, D Ruegamer, T Kneib, S Greven
R package version 0.3, 2018
Gradient Boosting for Linear Mixed Models
C Griesbach, B Säfken, E Waldmann
The International Journal of Biostatistics, 2021
Unsupervised document classification integrating web scraping, one-class SVM and LDA topic modelling
A Thielmann, C Weisser, A Krenz, B Säfken
Journal of Applied Statistics 50 (3), 574-591, 2023
TTLocVis: A Twitter Topic Location Visualization Package
G Kant, C Weisser, B Säfken
Journal of Open Source Software 5 (54), 2507, 2020
Stock price predictions with LSTM neural networks and twitter sentiment
ML Thormann, J Farchmin, C Weisser, RM Kruse, B Säfken, A Silbersdorff
Statistics, Optimization & Information Computing 9 (2), 268-287, 2021
Introductory data science across disciplines, using Python, case studies and industry consulting projects
J Lasser, D Manik, A Silbersdorff, B Säfken, T Kneib
Teaching Statistics, 2020
Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data
C Weisser, C Gerloff, A Thielmann, A Python, A Reuter, T Kneib, B Säfken
Computational Statistics, 1-28, 2022
Model averaging for linear mixed models via augmented Lagrangian
RM Kruse, A Silbersdorff, B Säfken
Computational Statistics & Data Analysis 167, 107351, 2022
Package ‘cAIC4’
B Saefken, D Ruegamer, P Baumann, RM Kruse
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean
A Thielmann, RM Kruse, T Kneib, B Säfken
arXiv preprint arXiv:2301.11862, 2023
Identifying topical shifts in twitter streams: an integration of non-negative matrix factorisation, sentiment analysis and structural break models for large scale data
M Luber, C Weisser, B Säfken, A Silbersdorff, T Kneib, K Kis-Katos
Disinformation in Open Online Media: Third Multidisciplinary International …, 2021
Conditional covariance penalties for mixed models
B Säfken, T Kneib
Scandinavian Journal of Statistics 47 (3), 990-1010, 2020
Learning deep
B Säfken, A Silbersdorff, C Weisser
Universitätsverlag Göttingen, 2020
Structural Neural Additive Models: Enhanced Interpretable Machine Learning
M Luber, A Thielmann, B Säfken
arXiv preprint arXiv:2302.09275, 2023
Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class
A Thielmann, C Weisser, B Säfken
arXiv preprint arXiv:2212.09422, 2022
Twitmo: A Twitter Data Topic Modeling and Visualization Package for R
A Buchmüller, G Kant, C Weisser, B Säfken, K Kis-Katos, T Kneib
arXiv preprint arXiv:2207.11236, 2022
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