Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Elisabeth Bergherr"'
Publikováno v:
Mathematics, Vol 11, Iss 2, p 411 (2023)
Modeling longitudinal data (e.g., biomarkers) and the risk for events separately leads to a loss of information and bias, even though the underlying processes are related to each other. Hence, the popularity of joint models for longitudinal and time-
Externí odkaz:
https://doaj.org/article/fbfa6311288f458d936246618a5c3e98
Publikováno v:
PLoS ONE, Vol 16, Iss 7, p e0254178 (2021)
Boosting techniques from the field of statistical learning have grown to be a popular tool for estimating and selecting predictor effects in various regression models and can roughly be separated in two general approaches, namely gradient boosting an
Externí odkaz:
https://doaj.org/article/4b52185acd62433d8f95dc84ac26e9e0
Publikováno v:
Computational Statistics. 37:2295-2332
Tuning of model-based boosting algorithms relies mainly on the number of iterations, while the step-length is fixed at a predefined value. For complex models with several predictors such as Generalized additive models for location, scale and shape (G
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ea93ee01fa5fc44c38d8b50af04a22c
https://doi.org/10.1007/s00180-022-01199-3
https://doi.org/10.1007/s00180-022-01199-3
Publikováno v:
Computational and Mathematical Methods in Medicine
Computational and Mathematical Methods in Medicine, Vol 2021 (2021)
Computational and Mathematical Methods in Medicine, Vol 2021 (2021)
Joint models are a powerful class of statistical models which apply to any data where event times are recorded alongside a longitudinal outcome by connecting longitudinal and time-to-event data within a joint likelihood allowing for quantification of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c68846b928456e3432e3be436aa2f0c
https://doi.org/10.1155/2021/4384035
https://doi.org/10.1155/2021/4384035
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
