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EnglishIn 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 | |
| Autori interni: | ||
| Data di pubblicazione: | 2013 | |
| Handle: | http://hdl.handle.net/20.500.11769/85764 | |
| ISBN: | 9788867871179 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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