Families of Parsimonious Finite Mixtures of Regression Models
This work provides a more efficient modeling approach for researchers analyzing heterogeneous regression data, but it is incremental as it builds on existing mixture models.
The authors tackled the problem of modeling heterogeneity in data with functional dependencies by extending finite mixtures of regression models through an eigen-decomposition on the error covariance matrix, resulting in parsimonious families that account for correlations between multiple responses, with performance validated on simulated and real data.
Finite mixtures of regression models offer a flexible framework for investigating heterogeneity in data with functional dependencies. These models can be conveniently used for unsupervised learning on data with clear regression relationships. We extend such models by imposing an eigen-decomposition on the multivariate error covariance matrix. By constraining parts of this decomposition, we obtain families of parsimonious mixtures of regressions and mixtures of regressions with concomitant variables. These families of models account for correlations between multiple responses. An expectation-maximization algorithm is presented for parameter estimation and performance is illustrated on simulated and real data.