Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/313158 
Year of Publication: 
2023
Citation: 
[Journal:] AStA Advances in Statistical Analysis [ISSN:] 1863-818X [Volume:] 108 [Issue:] 2 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2023 [Pages:] 351-373
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
In 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 R 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.
Subjects: 
Mixture models
Deep learning
Structured additive regression
Neural networks
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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