On the fitting of mixtures of multivariate skew t-distributions via the EM algorithm

TL;DR

This paper introduces an exact EM algorithm for fitting mixtures of multivariate skew t-distributions, avoiding Monte Carlo methods and enabling efficient modeling of complex, asymmetric, heavy-tailed data.

Contribution

The paper develops a novel EM algorithm that computes maximum likelihood estimates for mixtures of unrestricted multivariate skew t-distributions without Monte Carlo approximation.

Findings

Efficient computation of semi-infinite integrals via moments of truncated t-distributions.

Successful application to real flow cytometric data sets.

Elimination of Monte Carlo estimation in mixture model fitting.

Abstract

We show how the expectation-maximization (EM) algorithm can be applied exactly for the fitting of mixtures of general multivariate skew t (MST) distributions, eliminating the need for computationally expensive Monte Carlo estimation. Finite mixtures of MST distributions have proven to be useful in modelling heterogeneous data with asymmetric and heavy tail behaviour. Recently, they have been exploited as an effective tool for modelling flow cytometric data. However, without restrictions on the the characterizations of the component skew t-distributions, Monte Carlo methods have been used to fit these models. In this paper, we show how the EM algorithm can be implemented for the iterative computation of the maximum likelihood estimates of the model parameters without resorting to Monte Carlo methods for mixtures with unrestricted MST components. The fast calculation of semi-infinite integrals on the E-step of the EM algorithm is effected by noting that they can be put in the form of moments of the truncated multivariate t-distribution, which subsequently can be expressed in terms of the non-truncated form of the t-distribution function for which fast algorithms are available. We demonstrate the usefulness of the proposed methodology by some applications to three real data sets.