A block EM algorithm for multivariate skew normal and skew t-mixture models

TL;DR

This paper introduces a block EM algorithm for efficiently fitting multivariate skew normal and skew t-mixture models, leveraging parallel computation to significantly reduce processing time.

Contribution

It develops a block implementation of the EM algorithm that enables parallel processing of the E- and M-steps for skew mixture models, enhancing computational efficiency.

Findings

Significant reduction in computation time demonstrated on real datasets.

Parallel EM algorithm implementation suitable for multicore and multi-processor systems.

Compatible with existing multithreaded EM approaches for further speed-up.

Abstract

Finite mixtures of skew distributions provide a flexible tool for modelling heterogeneous data with asymmetric distributional features. However, parameter estimation via the Expectation-Maximization (EM) algorithm can become very time-consuming due to the complicated expressions involved in the E-step that are numerically expensive to evaluate. A more time-efficient implementation of the EM algorithm was recently proposed which allows each component of the mixture model to be evaluated in parallel. In this paper, we develop a block implementation of the EM algorithm that facilitates the calculations in the E- and M-steps to be spread across a larger number of threads. We focus on the fitting of finite mixtures of multivariate skew normal and skew t-distributions, and show that both the E- and M-steps in the EM algorithm can be modified to allow the data to be split into blocks. The approach can be easily implemented for use by multicore and multi-processor machines. It can also be applied concurrently with the recently proposed multithreaded EM algorithm to achieve further reduction in computation time. The improvement in time performance is illustrated on some real datasets.