From Generalized Linear Models to Neural Networks, and Back

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

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Year of publication:	2020
Authors:	Wuthrich, Mario V.
Publisher:	[2020]: [S.l.] : SSRN
Subject:	Schätztheorie \| Estimation theory \| Neuronale Netze \| Neural networks

Extent:	1 Online-Ressource (56 p)
Series:	This paper has been integrated into SSRN Manuscript ; 3822407
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments December 11, 2019 erstellt
Other identifiers:	10.2139/ssrn.3491790 [DOI]
Classification:	G22 - Insurance; Insurance Companies ; C45 - Neural Networks and Related Topics ; C52 - Model Evaluation and Testing ; C53 - Forecasting and Other Model Applications
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10012846635