An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling

Mixture-of-experts (MoE) models are a powerful paradigm for modeling of data arising from complex data generating processes (DGPs). In this article, we demonstrate how different MoE models can be constructed to approximate the underlying DGPs of arbitrary types of data. Due to the probabilistic nature of MoE models, we propose the maximum quasilikelihood (MQL) estimator as a method for estimating MoE model parameters from data, and we provide conditions under which MQL estimators are consistent and asymptotically normal. The blockwise minorization–maximizatoin (blockwise-MM) algorithm framework is proposed as an all-purpose method for constructing algorithms for obtaining MQL estimators. An example derivation of a blockwise-MM algorithm is provided. We then present a method for constructing information criteria for estimating the number of components in MoE models and provide ∗Department of Mathematics and Statistics, La Trobe University, 3086 Bundoora, Victoria Australia. Corresponding author. Email: [email protected]. †Normandie Universite, UNICAEN, CNRS, Laboratoire de Mathematiques Nicolas Oresme LMNO, 14000 Caen, France.