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Rügamer, D.* ; Kolb, C.* ; Fritz, C.* ; Pfisterer, F.* ; Kopper, P.* ; Bischl, B.* ; Shen, R.* ; Bukas, C. ; Barros De Andrade E Sousa, L. ; Thalmeier, D. ; Baumann, P.F.M.* ; Kook, L.* ; Klein, N.* ; Müller, C.L.

deepregression: A flexible neural network framework for semi-structured deep distributional regression.

J. Stat. Software 105, 1-31 (2023)
Postprint DOI
In this paper we describe the implementation of semi-structured deep distributional re-gression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks. Our implementation encompasses (1) a modular neural network building system based on the deep learning library TensorFlow for the fusion of various statistical and deep learning approaches, (2) an orthogonaliza-tion cell to allow for an interpretable combination of different subnetworks, as well as (3) pre-processing steps necessary to set up such models. The software package allows to define models in a user-friendly manner via a formula interface that is inspired by classical statistical model frameworks such as mgcv. The package’s modular design and function-ality provides a unique resource for both scalable estimation of complex statistical models and the combination of approaches from deep learning and statistics. This allows for state-of-the-art predictive performance while simultaneously retaining the indispensable interpretability of classical statistical models.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Additive Predictors ; Deep Learning ; Effect Decomposition ; Orthogonal Complement ; Penalization ; Smoothing
ISSN (print) / ISBN 1548-7660
Journal Journal of Statistical Software
Quellenangaben Volume: 105, Issue: 2, Pages: 1-31 Article Number: , Supplement: ,
Publisher University of California at Los Angeles
Publishing Place Ucla Dept Statistics, 8130 Math Sciences Bldg, Box 951554, Los Angeles, Ca 90095-1554 Usa
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Grants German research foundation (DFG) through the Emmy Noether grant
German Federal Ministry of Education and Research (BMBF)