Mixture of experts distributional regression: implementation using robust estimation with adaptive first-order methods

Author:

Rügamer DavidORCID,Pfisterer Florian,Bischl Bernd,Grün Bettina

Abstract

AbstractIn this work, we propose an efficient implementation of mixtures of experts distributional regression models which exploits robust estimation by using stochastic first-order optimization techniques with adaptive learning rate schedulers. We take advantage of the flexibility and scalability of neural network software and implement the proposed framework in mixdistreg, an software package that allows for the definition of mixtures of many different families, estimation in high-dimensional and large sample size settings and robust optimization based on TensorFlow. Numerical experiments with simulated and real-world data applications show that optimization is as reliable as estimation via classical approaches in many different settings and that results may be obtained for complicated scenarios where classical approaches consistently fail.

Funder

Bundesministerium für Bildung und Forschung

Ludwig-Maximilians-Universität München

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Economics and Econometrics,Social Sciences (miscellaneous),Modeling and Simulation,Statistics and Probability,Analysis

Reference44 articles.

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