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

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

additive predictors, deep learning, effect decomposition, orthogonal complement,
penalization, smoothing.

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.

David Rügamer

https://www.jstatsoft.org/article/view/v105i02

LMU Munich

January 2023

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