From Generalized Linear Models to Neural Networks, and Back
This paper has been integrated into SSRN Manuscript 3822407
Posted: 9 Dec 2019 Last revised: 24 Nov 2021
Date Written: December 11, 2019
We present how to enhance classical generalized linear models by neural network features. On the way there, we highlight the traps and pitfalls that need to be avoided to get good statistical models. This includes the non-uniqueness of sufficiently good regression models, the balance property, and representation learning, which brings us back to the concept of the good old generalized linear models. This paper has been integrated into SSRN Manuscript 3822407.
Keywords: generalized linear model, GLM, neural network, regression modeling, exponential dispersion family, deviance loss, balance property, canonical link, representation learning, regularization, LASSO, claims frequency modeling
JEL Classification: G22, C45, C52, C53
Suggested Citation: Suggested Citation