The cluster-weighted model (CWM) is a mixture model with random covariates that allows for flexible clustering/classification and distribution estimation of a random vector composed of a response variable and a set of covariates. Within this class of models, the generalized linear exponential CWMis here introduced especially for modeling bivariate data of mixed-type. Its natural counterpart in the family of latent class models is also defined. Maximum likelihood parameter estimates are derived using the expectation-maximization algorithm and some computational issues are detailed. Through Monte Carlo experiments, the classification performance of the proposed model is compared with other mixture-based approaches, consistency of the estimators of the regression coefficients is evaluated, and several likelihood-based information criteria are compared for selecting the number of mixture components. An application to real data is also finally considered.
|Titolo:||Clustering Bivariate Mixed-Type Data via the Cluster-Weighted Model|
|Data di pubblicazione:||2016|
|Citazione:||Clustering Bivariate Mixed-Type Data via the Cluster-Weighted Model / Punzo A.; Ingrassia S. - In: COMPUTATIONAL STATISTICS. - ISSN 0943-4062. - 31:3(2016), pp. 3.989-3.1013.|
|Appare nelle tipologie:||1.1 Articolo in rivista|