Author Säfken, Benjamin
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2016 | Journal Article |
Smoothing Parameter and Model Selection for General Smooth Models
Wood, S. N.; Pya, N. & Säfken, B. (2016)
Journal of the American Statistical Association, 111(516) pp. 1548-1563. DOI: https://doi.org/10.1080/01621459.2016.1180986
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2018 | Preprint
Conditional Model Selection in Mixed-Effects Models with cAIC4
Säfken, B.; Rügamer, D.; Kneib, T. & Greven, S. (2018)
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2019 | Journal Article |
Conditional covariance penalties for mixed models
Säfken, B. & Kneib, T. (2019)
Scandinavian Journal of Statistics, 47(3) pp. 990-1010. DOI: https://doi.org/10.1111/sjos.12437
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2020 | Book Chapter
Sign Language Recognition Using Regularized Convolutional Neural Networks
Thielmann, A.; Seifert, Q. E. & Lichter, J. (2020)
In:Säfken, Benjamin; Silbersdorff, Alexander; Weisser, Christoph (Eds.), Learning Deep - Perspectives on Deep Learning Algorithms and Artificial Intelligence.
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2020 | Anthology |
Learning deep: Perspectives on Deep Learning Algorithms and Artificial Intelligence
Säfken, B.; Silbersdorff, A. & Weisser, C. (Eds.) (2020)
Göttingen: Universitätsverlag Göttingen. DOI: https://doi.org/10.17875/gup2020-1338
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2021 | Journal Article
Unsupervised document classification integrating web scraping, one-class SVM and LDA topic modelling
Thielmann, A.; Weisser, C.; Krenz, A. & Säfken, B. (2021)
Journal of Applied Statistics, 50(3) pp. 574-591. DOI: https://doi.org/10.1080/02664763.2021.1919063
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2021 | Journal Article
Conditional Model Selection in Mixed-Effects Models with cAIC4
Säfken, B.; Rügamer, D.; Kneib, T. & Greven, S. (2021)
Journal of Statistical Software, 99(8). DOI: https://doi.org/10.18637/jss.v099.i08
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2021 | Anthology |
Learning Deep Textwork: Perspectives on Natural Language Processing and Artificial Intelligence
Kruse, R.-M.; Säfken, B.; Silbersdorff, A. & Weisser, C. (Eds.) (2021)
Göttingen: Universitätsverlag Göttingen. DOI: https://doi.org/10.17875/gup2021-1608
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2021 | Book Chapter
Identifying Topical Shifts in Twitter Streams: An Integration of Non-negative Matrix Factorisation, Sentiment Analysis and Structural Break Models for Large Scale Data
Luber, M.; Weisser, C.; Säfken, B.; Silbersdorff, A.; Kneib, T.& Kis-Katos, K. (2021)
In:Bright, Jonathan; Giachanou, Anastasia; Spaiser, Viktoria; Spezzano, Francesca; George, Anna; Pavliuc, Alexandra (Eds.), Disinformation in Open Online Media : Third Multidisciplinary International Symposium, MISDOOM 2021, Virtual Event, September 21–22, 2021, Proceedings pp. 33-49. Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-87031-7_3
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2021 | Journal Article
Stock Price Predictions with LSTM Neural Networks and Twitter Sentiment
Thormann, M.-L.; Farchmin, J.; Weisser, C.; Kruse, R.-M.; Säfken, B. & Silbersdorff, A. (2021)
Statistics, Optimization and Information Computing, 9(2) pp. 268-287. DOI: https://doi.org/10.19139/soic-2310-5070-1202
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2021 | Journal Article | Research Paper
Gradient boosting for linear mixed models
Griesbach, C. ; Säfken, B. & Waldmann, E. (2021)
The International Journal of Biostatistics, 17(2) pp. 317-329. DOI: https://doi.org/10.1515/ijb-2020-0136
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2021 | Journal Article | Research Paper |
Introductory data science across disciplines, using Python, case studies, and industry consulting projects
Lasser, J.; Manik, D.; Silbersdorff, A. ; Säfken, B. & Kneib, T. (2021)
Teaching Statistics, 43 pp. S190-S200. DOI: https://doi.org/10.1111/test.12243
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2022 | Journal Article
Model averaging for linear mixed models via augmented Lagrangian
Kruse, R.-M.; Silbersdorff, A. & Säfken, B. (2022)
Computational Statistics & Data Analysis, 167 art. S0167947321001857. DOI: https://doi.org/10.1016/j.csda.2021.107351
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2022 | Journal Article | Research Paper |
An iterative topic model filtering framework for short and noisy user-generated data: analyzing conspiracy theories on twitter
Kant, G.; Wiebelt, L.; Weisser, C.; Kis-Katos, K. ; Luber, M. & Säfken, B. (2022)
International Journal of Data Science and Analytics,. DOI: https://doi.org/10.1007/s41060-022-00321-4
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2022 | Journal Article |
Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data
Weisser, C.; Gerloff, C.; Thielmann, A.; Python, A.; Reuter, A.; Kneib, T. & Säfken, B. (2022)
Computational Statistics,. DOI: https://doi.org/10.1007/s00180-022-01246-z
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2022 | Preprint |
Penalisierte Regressions-Splines in Mischungsdichtenetzwerken
Seifert, Q. E. ; Thielmann, A.; Bergherr, E. ; Säfken, B.; Zierk, J.; Rauh, M.& Hepp, T. (2022). DOI: https://doi.org/10.21203/rs.3.rs-2398185/v1
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2023 | Journal Article |
Erwartete Schulprobleme als Folge der Corona-Schulschließungen im Frühjahr 2020 – Empirische Evidenz zur Bedeutung familialer Ressourcen mittels nichtlinearer Modellierung
Lorenz, J.; Ike, S.; Dammann, L. M.; Becker, D.; Säfken, B. & Silbersdorff, A. (2023)
Zeitschrift für Erziehungswissenschaft, 26(2) pp. 403-441. DOI: https://doi.org/10.1007/s11618-023-01149-9
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2023 | Journal Article
Rage Against the Mean – A Review of Distributional Regression Approaches
Kneib, T.; Silbersdorff, A. & Säfken, B. (2023)
Econometrics and Statistics, 26 pp. 99-123. DOI: https://doi.org/10.1016/j.ecosta.2021.07.006
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2023 | Conference Paper
Coherence based Document Clustering
Thielmann, A.; Weisser, C.; Kneib, T. & Säfken, B. (2023)
pp. 9-16. 2023 IEEE 17th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA.
IEEE. DOI: https://doi.org/10.1109/ICSC56153.2023.00009
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2023 | Book Chapter
Topic Model—Machine Learning Classifier Integrations on Geocoded Twitter Data
Kant, G.; Weisser, C.; Kneib, T.& Säfken, B. (2023)
In:Phuong, Nguyen Hoang; Kreinovich, Vladik (Eds.), Biomedical and Other Applications of Soft Computing pp. 105-120. Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-031-08580-2_11
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