Mixtures of multivariate generalized linear models with overlapping clusters

TL;DR

This paper introduces a novel mixture model with overlapping clusters for multivariate generalized linear models, enabling more flexible grouping in complex heterogeneous datasets, demonstrated through simulations and real-world voting data.

Contribution

It proposes a new mixture model with overlapping clusters and an efficient MCMC algorithm, extending traditional non-overlapping clustering approaches in regression analysis.

Findings

Effective in modeling overlapping clusters in multivariate data

Demonstrated on a two-mode network with probit regression

Applied to US Supreme Court voting behavior

Abstract

With the advent of ubiquitous monitoring and measurement protocols, studies have started to focus more and more on complex, multivariate and heterogeneous datasets. In such studies, multivariate response variables are drawn from a heterogeneous population often in the presence of additional covariate information. In order to deal with this intrinsic heterogeneity, regression analyses have to be clustered for different groups of units. Up until now, mixture model approaches assigned units to distinct and non-overlapping groups. However, not rarely these units exhibit more complex organization and clustering. It is our aim to define a mixture of generalized linear models with overlapping clusters of units. This involves crucially an overlap function, that maps the coefficients of the parent clusters into the the coefficient of the multiple allocation units. We present a computationally efficient MCMC scheme that samples the posterior distribution of the parameters in the model. An example on a two-mode network study shows details of the implementation in the case of a multivariate probit regression setting. A simulation study shows the overall performance of the method, whereas an illustration of the voting behaviour on the US supreme court shows how the 9 justices split in two overlapping sets of justices.