Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Benjamin Säfken"'
Publikováno v:
Journal of Statistical Software, Vol 99, Iss 1 (2021)
Model selection in mixed models based on the conditional distribution is appropriate for many practical applications and has been a focus of recent statistical research. In this paper we introduce the R package cAIC4 that allows for the computation o
Externí odkaz:
https://doaj.org/article/449aaf01f22e44c585b1a1d9b566554c
Publikováno v:
Econometrics and Statistics. 26:99-123
Distributional regression models that overcome the traditional focus on relating the conditional mean of the response to explanatory variables and instead target either the complete conditional response distribution or more general features thereof h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::408e4b2194d108317555b711495c8849
https://doi.org/10.1016/j.ecosta.2021.07.006
https://doi.org/10.1016/j.ecosta.2021.07.006
Autor:
Christoph Weisser, Christoph Gerloff, Anton Thielmann, Andre Python, Arik Reuter, Thomas Kneib, Benjamin Säfken
Publikováno v:
Computational Statistics. 38:647-674
Topic models are a useful and popular method to find latent topics of documents. However, the short and sparse texts in social media micro-blogs such as Twitter are challenging for the most commonly used Latent Dirichlet Allocation (LDA) topic model.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd0649f062a56bc8388b122b216dd03d
https://doi.org/10.1007/s00180-022-01246-z
https://doi.org/10.1007/s00180-022-01246-z
Publikováno v:
2023 IEEE 17th International Conference on Semantic Computing (ICSC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d8a1ed6e1fed67f109f3a8c6aaf94230
https://doi.org/10.1109/icsc56153.2023.00009
https://doi.org/10.1109/icsc56153.2023.00009
Autor:
Quentin Edward Seifert, Anton Thielmann, Elisabeth Bergherr, Benjamin Säfken, Jakob Zierk, Manfred Rauh, Tobias Hepp
Mixture Density Networks (MDN) belong to a class of models that can be applied to data which cannot be sufficiently described by a single distribution since it originates from different components of the main unit and therefore needs to be described
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bddae33f3cf84494035cd35a12d33f21
https://doi.org/10.21203/rs.3.rs-2398185/v1
https://doi.org/10.21203/rs.3.rs-2398185/v1
Publikováno v:
Biomedical and Other Applications of Soft Computing ISBN: 9783031085796
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e1c0fc2cef6f31b52e4d6d488ad3c85e
https://doi.org/10.1007/978-3-031-08580-2_11
https://doi.org/10.1007/978-3-031-08580-2_11
Autor:
Marah-Lisanne Thormann, Alexander Silbersdorff, René-Marcel Kruse, Christoph Weisser, Jan Farchmin, Benjamin Säfken
Publikováno v:
Statistics, Optimization & Information Computing. 9:268-287
Predicting the trend of stock prices is a central topic in financial engineering. Given the complexity and nonlinearity of the underlying processes we consider the use of neural networks in general and sentiment analysis in particular for the analysi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ed36aa799714538c35b8ce7dddadb637
https://doi.org/10.19139/soic-2310-5070-1202
https://doi.org/10.19139/soic-2310-5070-1202
Autor:
Benjamin Säfken, Thomas Kneib
Publikováno v:
Scandinavian Journal of Statistics. 47:990-1010
The prediction error for mixed models can have a conditional or a marginal perspective depending on the research focus. We introduce a novel conditional version of the optimism theorem for mixed models linking the conditional prediction error to cova
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::143760b9cebf9849c8df7d4f61804d33
https://doi.org/10.1111/sjos.12437
https://doi.org/10.1111/sjos.12437
Publikováno v:
Teaching Statistics. 43
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7911c4ea328e6b6d893a8037bfb84146
https://doi.org/10.1111/test.12243
https://doi.org/10.1111/test.12243
Autor:
Gillian Kant, Levin Wiebelt, Christoph Weisser, Krisztina Kis-Katos, Mattias Luber, Benjamin Säfken
Publikováno v:
International journal of data science and analytics.
Conspiracy theories have seen a rise in popularity in recent years. Spreading quickly through social media, their disruptive effect can lead to a biased public view on policy decisions and events. We present a novel approach for LDA-pre-processing ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a049f54a8f9aca26c504eb7d5d457d13
https://pubmed.ncbi.nlm.nih.gov/35542313
https://pubmed.ncbi.nlm.nih.gov/35542313