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bamlss : A Lego Toolbox for Flexible Bayesian Regression (and Beyond)

Umlauf, Nikolaus; Klein, Nadja ORCID iD icon 1; Simon, Thorsten; Zeileis, Achim
1 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)

Abstract:

Over the last decades, the challenges in applied regression and in predictive modeling have been changing considerably: (1) More flexible regression model specifications are needed as data sizes and available information are steadily increasing, consequently demanding for more powerful computing infrastructure. (2) Full probabilistic models by means of distributional regression - rather than predicting only some underlying individual quantities from the distributions such as means or expectations - is crucial in many applications. (3) Availability of Bayesian inference has gained in importance both as an appealing framework for regularizing or penalizing complex models and estimation therein as well as a natural alternative to classical frequentist inference. However, while there has been a lot of research on all three challenges and the development of corresponding software packages, a modular software implementation that allows to easily combine all three aspects has not yet been available for the general framework of distributional regression. To fill this gap, the R package bamlss is introduced for Bayesian additive models for location, scale, and shape (and beyond) - with the name reflecting the most important distributional quantities (among others) that can be modeled with the software. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000175452
Veröffentlicht am 23.10.2024
Originalveröffentlichung
DOI: 10.18637/jss.v100.i04
Scopus
Zitationen: 15
Web of Science
Zitationen: 16
Dimensions
Zitationen: 26
Cover der Publikation
Zugehörige Institution(en) am KIT Scientific Computing Center (SCC)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 30.11.2021
Sprache Englisch
Identifikator ISSN: 1548-7660
KITopen-ID: 1000175452
HGF-Programm 46.21.02 (POF IV, LK 01) Cross-Domain ATMLs and Research Groups
Erschienen in Journal of Statistical Software
Verlag Foundation for Open Access Statistics
Band 100
Heft 4
Projektinformation ENP, 1. Förderabschnitt (DFG, DFG EIN, KL 3037/1-1)
Nachgewiesen in Scopus
Web of Science
Dimensions
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