In model-based clustering and classification, the cluster-weighted model is a convenient approach when the random vector of interest is constituted by a response variable <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$Y$$</EquationSource> </InlineEquation> and by a vector <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$${\varvec{X}}$$</EquationSource> </InlineEquation> of <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$$p$$</EquationSource> </InlineEquation> covariates. However, its applicability may be limited when <InlineEquation ID="IEq4"> <EquationSource Format="TEX">$$p$$</EquationSource> </InlineEquation> is...</equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation>

Parameter estimation for model-based clustering using a finite mixture of normal inverse Gaussian (NIG) distributions is achieved through variational Bayes approximations. Univariate NIG mixtures and multivariate NIG mixtures are considered. The use of variational Bayes approximations here is a...