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Boosting multivariate structured additive distributional regression models

Strömer, Annika; Klein, Nadja ORCID iD icon; Staerk, Christian; Klinkhammer, Hannah; Mayr, Andreas

Abstract:

We develop a model-based boosting approach for multivariate distributional regression within the framework of generalized additive models for location, scale, and shape. Our approach enables the simultaneous modeling of all distribution parameters of an arbitrary parametric distribution of a multivariate response conditional on explanatory variables, while being applicable to potentially high-dimensional data. Moreover, the boosting algorithm incorporates data-driven variable selection, taking various different types of effects into account. As a special merit of our approach, it allows for modeling the association between multiple continuous or discrete outcomes through the relevant covariates. After a detailed simulation study investigating estimation and prediction performance, we demonstrate the full flexibility of our approach in three diverse biomedical applications. The first is based on high-dimensional genomic cohort data from the UK Biobank, considering a bivariate binary response (chronic ischemic heart disease and high cholesterol). Here, we are able to identify genetic variants that are informative for the association between cholesterol and heart disease. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000175293
Veröffentlicht am 18.10.2024
Originalveröffentlichung
DOI: 10.1002/sim.9699
Scopus
Zitationen: 2
Web of Science
Zitationen: 1
Dimensions
Zitationen: 8
Cover der Publikation
Zugehörige Institution(en) am KIT Karlsruher Institut für Technologie (KIT)
Scientific Computing Center (SCC)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 20.05.2023
Sprache Englisch
Identifikator ISSN: 0277-6715, 1097-0258
KITopen-ID: 1000175293
HGF-Programm 46.21.02 (POF IV, LK 01) Cross-Domain ATMLs and Research Groups
Erschienen in Statistics in Medicine
Verlag John Wiley and Sons
Band 42
Heft 11
Seiten 1779–1801
Projektinformation Boosting Copulas (DFG, DFG EIN, KL3037/2-1)
Vorab online veröffentlicht am 17.03.2023
Nachgewiesen in Scopus
Web of Science
Dimensions
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