deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression

Open access
Datum
2023-01-18Typ
- Journal Article
ETH Bibliographie
yes
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Abstract
In this paper we describe the implementation of semi-structured deep distributional regression, 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 orthogonalization 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 functionality 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. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000597165Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
Journal of Statistical SoftwareBand
Seiten / Artikelnummer
Verlag
UCLA, Department of StatisticsThema
Additive predictors; Deep learning; Effect decomposition; Orthogonal complement; Penalization; SmoothingOrganisationseinheit
06336 - KOF FB Data Science und Makroökon. Meth. / KOF FB Data Science and Macroec. Methods
02525 - KOF Konjunkturforschungsstelle / KOF Swiss Economic Institute
ETH Bibliographie
yes
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