Professur für Raumbezogene Datenanalyse und Statistische Lernverfahren

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  • 2023 | Preprint
    ​ ​"Spatial Joint Models through Bayesian Structured Piece-wise Additive Joint Modelling for Longitudinal and Time-to-Event Data"​
    Rappl, A.; Kneib, T. ; Lang, S.& Bergherr, E. ​ (2023)
    ​Details 
  • 2023 | Journal Article | 
    ​ ​MELD-score for risk stratification in cardiac surgery​
    Pathare, P.; Elbayomi, M.; Weyand, M.; Griesbach, C.   & Harig, F.​ (2023) 
    Heart and Vessels,.​ DOI: https://doi.org/10.1007/s00380-023-02262-9 
    ​Details  DOI  PMID  PMC 
  • 2023 | Journal Article | 
    ​ ​Variable Selection and Allocation in Joint Models via Gradient Boosting Techniques​
    Griesbach, C. ; Mayr, A. & Bergherr, E. ​ (2023) 
    Mathematics, 11(2) pp. 411​.​ DOI: https://doi.org/10.3390/math11020411 
    ​Details  DOI 
  • 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 
    ​Details  DOI 
  • 2022 | Conference Paper | 
    ​ ​Gradient boosting for linear mixed effects logit models​
    Knieper, L. ; Griesbach, C.   & Bergherr, E.​ (2022)
    ​Statistical Computing 2022​, Schloss Reisensburg (Günzburg.
    ​Details 
  • 2022 | Conference Paper | 
    ​ ​Modeling the prenatal care of women in West Africa by a GAMLSS using a gradient boosting algorithm with an adaptive step-length​
    Daub, A. ; Zhang, B. ; Griesbach, C.   & Bergherr, E.​ (2022)
    ​Statistical Computing 2022​, Schloss Reisensburg (Günzburg).
    ​Details 
  • 2022 | Journal Article
    ​ ​Bayesian learners in gradient boosting for linear mixed models​
    Zhang, B. ; Griesbach, C.   & Bergherr, E. ​ (2022) 
    The International Journal of Biostatistics, 0(0).​ DOI: https://doi.org/10.1515/ijb-2022-0029 
    ​Details  DOI  PMID  PMC 
  • 2022 | Journal Article | Research Paper | 
    ​ ​Adaptive step-length selection in gradient boosting for Gaussian location and scale models​
    Zhang, B. ; Hepp, T.; Greven, S. & Bergherr, E. ​ (2022) 
    Computational Statistics,.​ DOI: https://doi.org/10.1007/s00180-022-01199-3 
    ​Details  DOI 
  • 2022 | Journal Article | Research Paper | 
    ​ ​Aspirin improves transplant-free survival after TIPS implantation in patients with refractory ascites: a retrospective multicentre cohort study​
    Seifert, L. L.; Schindler, P.; Sturm, L.; Gu, W.; Seifert, Q. E. ; Weller, J. F. & Jansen, C. et al.​ (2022) 
    Hepatology International,.​ DOI: https://doi.org/10.1007/s12072-022-10330-x 
    ​Details  DOI 
  • 2021 | Journal Article | Research Paper
    ​ ​Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques​
    Griesbach, C. ; Groll, A. & Bergherr, E. ​ (2021) 
    PLoS One, 16(7) art. e0254178​.​ DOI: https://doi.org/10.1371/journal.pone.0254178 
    ​Details  DOI  PMID  PMC 
  • 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 
    ​Details  DOI  PMID  PMC 
  • 2021 | Journal Article | Research Paper | 
    ​ ​Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques​
    Griesbach, C. ; Groll, A. & Bergherr, E. ​ (2021) 
    Computational and Mathematical Methods in Medicine, 2021.​ DOI: https://doi.org/10.1155/2021/4384035 
    ​Details  DOI  PMID  PMC 
  • 2020 | Conference Paper
    ​ ​Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques​
    Griesbach, C. ; Groll, A. & Waldmann, E. ​ (2020)
    ​Proceedings of the 35th International Workshop on Statistical Modelling. ​35th International Workshop on Statistical Modelling, IWSM2020​, Bilbao, Spain.
    ​Details 
  • 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. 
    ​Details 

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