Recently, different mixture models have been proposed for multilevel data, generally requiring the local independence assumption. In this work, this assumption is relaxed by allowing each mixture component at the lower level of the hierarchical structure to be modeled according to a multivariate Gaussian distribution with a non-diagonal covariance matrix. For high dimensional problems, this solution can lead to highly parameterized models. In this proposal, the trade-off between model parsimony and flexibility is governed by assuming a latent factor generative model.
Daniela G. Calò, Cinzia Viroli (2010). A dimensionally reduced finite mixture model for multilevel data. JOURNAL OF MULTIVARIATE ANALYSIS, 101, 2543-2553 [10.1016/j.jmva.2010.07.003].
A dimensionally reduced finite mixture model for multilevel data
CALO', DANIELA GIOVANNA;VIROLI, CINZIA
2010
Abstract
Recently, different mixture models have been proposed for multilevel data, generally requiring the local independence assumption. In this work, this assumption is relaxed by allowing each mixture component at the lower level of the hierarchical structure to be modeled according to a multivariate Gaussian distribution with a non-diagonal covariance matrix. For high dimensional problems, this solution can lead to highly parameterized models. In this proposal, the trade-off between model parsimony and flexibility is governed by assuming a latent factor generative model.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.