Zobrazeno 1 - 10
of 243
pro vyhledávání: '"Foraita Ronja"'
High-dimensional networks play a key role in understanding complex relationships. These relationships are often dynamic in nature and can change with multiple external factors (e.g., time and groups). Methods for estimating graphical models are often
Externí odkaz:
http://arxiv.org/abs/2407.19978
We present an Alternating Direction Method of Multipliers (ADMM) algorithm designed to solve the Weighted Generalized Fused LASSO Signal Approximator (wFLSA). First, we show that wFLSAs can always be reformulated as a Generalized LASSO problem. With
Externí odkaz:
http://arxiv.org/abs/2407.18077
Methods of causal discovery aim to identify causal structures in a data driven way. Existing algorithms are known to be unstable and sensitive to statistical errors, and are therefore rarely used with biomedical or epidemiological data. We present an
Externí odkaz:
http://arxiv.org/abs/2406.19503
Despite extensive safety assessments of drugs prior to their introduction to the market, certain adverse drug reactions (ADRs) remain undetected. The primary objective of pharmacovigilance is to identify these ADRs (i.e., signals). In addition to tra
Externí odkaz:
http://arxiv.org/abs/2404.14213
Variable selection in linear regression settings is a much discussed problem. Best subset selection (BSS) is often considered the intuitive 'gold standard', with its use being restricted only by its NP-hard nature. Alternatives such as the least abso
Externí odkaz:
http://arxiv.org/abs/2302.12034
Autor:
Goerdten, Jantje, Muli, Samuel, Rattner, Jodi, Merdas, Mira, Achaintre, David, Yuan, Li, De Henauw, Stefaan, Foraita, Ronja, Hunsberger, Monica, Huybrechts, Inge, Lissner, Lauren, Molnár, Dénes, Moreno, Luis A, Russo, Paola, Veidebaum, Toomas, Aleksandrova, Krasimira, Nöthlings, Ute, Oluwagbemigun, Kolade, Keski-Rahkonen, Pekka, Floegel, Anna
Publikováno v:
In The Journal of Nutrition November 2024 154(11):3274-3285
In this guide, we present how to perform constraint-based causal discovery using three popular software packages: pcalg (with add-ons tpc and micd), bnlearn, and TETRAD. We focus on how these packages can be used with observational data and in the pr
Externí odkaz:
http://arxiv.org/abs/2108.13395
Causal discovery algorithms estimate causal graphs from observational data. This can provide a valuable complement to analyses focussing on the causal relation between individual treatment-outcome pairs. Constraint-based causal discovery algorithms r
Externí odkaz:
http://arxiv.org/abs/2108.13331
Publikováno v:
BMC Medical Research Methodology, Vol 10, Iss 1, p 10 (2010)
Abstract Background Primary prevention programmes are of increasing importance to reduce the impact of chronic diseases on the individual, institutional and societal level. However, most initiatives that develop and implement primary prevention progr
Externí odkaz:
https://doaj.org/article/7e43dc3966ad47b6917b0d5c6b7579ec
Autor:
Foraita, Ronja1 (AUTHOR) foraita@leibniz-bips.de, Witte, Janine1,2 (AUTHOR), Börnhorst, Claudia1 (AUTHOR), Gwozdz, Wencke3,4 (AUTHOR), Pala, Valeria5 (AUTHOR), Lissner, Lauren6 (AUTHOR), Lauria, Fabio7 (AUTHOR), Reisch, Lucia A.1,8 (AUTHOR), Molnár, Dénes9 (AUTHOR), De Henauw, Stefaan10 (AUTHOR), Moreno, Luis11 (AUTHOR), Veidebaum, Toomas12 (AUTHOR), Tornaritis, Michael13 (AUTHOR), Pigeot, Iris1,2 (AUTHOR), Didelez, Vanessa1,2 (AUTHOR)
Publikováno v:
Scientific Reports. 3/21/2024, Vol. 14 Issue 1, p1-14. 14p.