In the class of mixtures with random covariates, the generalized linear Gaussian cluster-weighted model (GLGCWM) has been recently proposed; in each mixture component, it models the response variable within the exponential family of distributions and the vector of real-valued covariates according to the multivariate Gaussian distribution. Due to the number of free parameters of each covariance matrix of the component Gaussian distributions, a family of fourteen parsimonious GLGCWMs is here introduced by applying some constraints on the eigen decomposition of these matrices. This novel family of models is also applied to a real data set where it gives good classification performance, especially when compared with more established mixture-based approaches.
|Titolo:||Parsimony in Mixtures with Random Covariates|
|Data di pubblicazione:||2013|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|