Mixture of Experts Distributional Regression: Implementation Using Robust Estimation with Adaptive First-order Methods

TL;DR

This paper introduces a scalable, robust implementation of mixtures of experts distributional regression models using stochastic optimization and adaptive learning, implemented in an R package for complex, high-dimensional data.

Contribution

It presents a novel, efficient framework for distributional regression with mixtures, leveraging neural network tools and robust optimization in R.

Findings

Optimization matches classical methods in reliability across various settings.

The approach enables modeling in complex scenarios where traditional methods fail.

The implementation is available in the mixdistreg R package.

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.