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...

We consider the problem of minimizing an objective function that depends on an orthonormal matrix. This situation is encountered, for example, when looking for common principal components. The Flury method is a popular approach but is not effective for higher dimensional problems. We obtain...

The efficacy of family-based approaches to mixture model-based clustering and classification depends on the selection of parsimonious models. Current wisdom suggests the Bayesian information criterion (BIC) for mixture model selection. However, the BIC has well-known limitations, including a...

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>

We introduce a dimension reduction method for model-based clustering obtained from a finite mixture of <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$$t$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mrow> <mi>t</mi> </mrow> </math> </EquationSource> </InlineEquation>-distributions. This approach is based on existing work on reducing dimensionality in the case of finite Gaussian mixtures. The method relies on identifying a reduced subspace of...</equationsource></equationsource></inlineequation>

Failure time models are considered when there is a subpopulation of individuals that is immune, or not susceptible, to an event of interest. Such models are of considerable interest in biostatistics. The most common approach is to postulate a proportion p of immunes or long-term survivors and to...

As data sets continue to grow in size and complexity, effective and efficient techniques are needed to target important features in the variable space. Many of the variable selection techniques that are commonly used alongside clustering algorithms are based upon determining the best variable...

This study investigates the economics of supplying cellulosic crop residue feedstocks within 100mile (or 161km) radius of a potential new biorefinery in southeast North Dakota. We utilize linear programming transportation models and GIS (Geographic Information System) maps to identify optimal...

This study investigates the economics of supplying wheat straw and corn stover within 100 mile radius of a potential new biorefinery in southeast North Dakota. In particular, straw and stover total delivery costs, potential straw and stover supply sites and least cost transportation routes are...