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pro vyhledávání: '"Griesbach, Colin"'
Model-based component-wise gradient boosting is a popular tool for data-driven variable selection. In order to improve its prediction and selection qualities even further, several modifications of the original algorithm have been developed, that main
Externí odkaz:
http://arxiv.org/abs/2302.13822
Boosting methods are widely used in statistical learning to deal with high-dimensional data due to their variable selection feature. However, those methods lack straightforward ways to construct estimators for the precision of the parameters such as
Externí odkaz:
http://arxiv.org/abs/2106.04862
Gradient boosting from the field of statistical learning is widely known as a powerful framework for estimation and selection of predictor effects in various regression models by adapting concepts from classification theory. Current boosting approach
Externí odkaz:
http://arxiv.org/abs/2011.00947
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:
http://arxiv.org/abs/1912.06382
In various data situations joint models are an efficient tool to analyze relationships between time dependent covariates and event times or to correct for event-dependent dropout occurring in regression analysis. Joint modeling connects a longitudina
Externí odkaz:
http://arxiv.org/abs/1810.10239
Publikováno v:
International Journal of Biostatistics; May2024, Vol. 20 Issue 1, p293-314, 22p
Publikováno v:
International Journal of Biostatistics; May2024, Vol. 20 Issue 1, p123-141, 19p
Autor:
Griesbach, Colin1 (AUTHOR) colin.griesbach@uni-goettingen.de, Mayr, Andreas2 (AUTHOR), Bergherr, Elisabeth1 (AUTHOR)
Publikováno v:
Mathematics (2227-7390). Jan2023, Vol. 11 Issue 2, p411. 16p.
Akademický článek
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Publikováno v:
Computational & Mathematical Methods in Medicine. 11/15/2021, p1-11. 11p.