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

Author

Listed:
  • David Rügamer

    (LMU Munich
    TU Dortmund
    Munich Center for Machine Learning)

  • Florian Pfisterer

    (LMU Munich
    Munich Center for Machine Learning)

  • Bernd Bischl

    (LMU Munich
    Munich Center for Machine Learning)

  • Bettina Grün

    (WU Vienna)

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.

Suggested Citation

  • David Rügamer & Florian Pfisterer & Bernd Bischl & Bettina Grün, 2024. "Mixture of experts distributional regression: implementation using robust estimation with adaptive first-order methods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(2), pages 351-373, June.
    • Handle: RePEc:spr:alstar:v:108:y:2024:i:2:d:10.1007_s10182-023-00486-8
    DOI: 10.1007/s10182-023-00486-8
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