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Model-Based Clustering
TLDR
A review of work to date in model-based clustering, from the famous paper by Wolfe in 1965 to work that is currently available only in preprint form, and a look ahead to the next decade or so. Expand
Parsimonious Gaussian mixture models
TLDR
A class of eight parsimonious Gaussian mixture models which are based on the mixtures of factor analyzers model are introduced and the maximum likelihood estimates for the parameters in these models are found using an AECM algorithm. Expand
Mixture Model-Based Classification
A Mixture of Generalized Hyperbolic Distributions
We introduce a mixture of generalized hyperbolic distributions as an alternative to the ubiquitous mixture of Gaussian distributions as well as their near relatives of which the mixture ofExpand
Model-based clustering of longitudinal data
Model-based clustering is a method of clustering data based on mixture modeling. Within the popular literature, a Gaussian mixture model is most frequently used. A common approach to model-basedExpand
Model-based clustering of microarray expression data via latent Gaussian mixture models
TLDR
This modelling approach builds on previous work by introducing a modified factor analysis covariance structure, leading to a family of 12 mixture models, including parsimonious models, which gives very good performance, relative to existing popular clustering techniques, when applied to real gene expression microarray data. Expand
Review and implementation of cure models based on first hitting times for Wiener processes
TLDR
This paper studies the Wiener process with negative drift as a possible cure rate model but the resulting defective inverse Gaussian model is found to provide a poor fit in some cases, and several possible modifications are suggested, which improve the defective inverseGaussian. Expand
Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models
TLDR
Model-based clustering using a family of Gaussian mixture models, with parsimonious factor analysis-like covariance structure, is described and an ecient algorithm for its implementation is presented, showing its eectiveness when compared to existing software. Expand
Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions
TLDR
A novel family of mixture models wherein each component is modeled using a multivariate t-distribution with an eigen-decomposed covariance structure is put forth, known as the tEIGEN family. Expand
Model-based classification using latent Gaussian mixture models
A novel model-based classification technique is introduced based on parsimonious Gaussian mixture models (PGMMs). PGMMs, which were introduced recently as a model-based clustering technique, ariseExpand
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