A mixture of SDB skew-t factor analyzers

Mixtures of skew-t distributions offer a flexible choice for model-based clustering. A mixture model of this sort can be implemented using a variety of formulations of the skew-t distribution. A mixture of skew-t factor analyzers model for clustering of high-dimensional data using a flexible formulation of the skew-t distribution is developed. Methodological details of the proposed approach, which represents an extension of the mixture of factor analyzers model to a flexible skew-t distribution, are outlined and details of parameter estimation are provided. Clustering results are illustrated and compared to an alternative formulation of the mixture of skew-t factor analyzers model as well as the mixture of factor analyzers model.

• Publication year

  2013

• Venue

  Econometrics and Statistics

• Publication date

  2013-10-23

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1016/j.ecosta.2017.05.001arXiv 1310.6224
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• A Mixture of Variance-Gamma Factor Analyzers

  2017cited by this paper
• A matrix variate skew‐t distribution

  2017cited by this paper
• Clustering with the multivariate normal inverse Gaussian distribution

  2016cited by this paper
• Model-Based Clustering

  2016cited by this paper
• Comment on "On Nomenclature, and the Relative Merits of Two Formulations of Skew Distributions" by A. Azzalini, R. Browne, M. Genton, and P. McNicholas

  2016cited by this paper
• The proof and measurement of association between two things.

  2015cited by this paper
• Location and scale mixtures of Gaussians with flexible tail behaviour: Properties, inference and application to multivariate clustering

  2015cited by this paper
• A robust factor analysis model using the restricted skew-$$t$$t distribution

  2015cited by this paper
• Multivariate data analysis as a discriminating method of the origin of wines

  2015cited by this paper
• On nomenclature for, and the relative merits of, two formulations of skew distributions

  2014cited by this paper
• Finite mixtures of canonical fundamental skew $$t$$t-distributions

  2014cited by this paper
• Finite mixtures of multivariate skew t-distributions: some recent and new results

  2014cited by this paper
• Model-based clustering of high-dimensional data: A review

  2014cited by this paper
• Mixtures of Multiple Scaled Generalized Hyperbolic Distributions

  2014cited by this paper
• Mixtures of Variance-Gamma Distributions

  2013influential reference
• A mixture of generalized hyperbolic distributions

  2013cited by this paper
• Flexible mixture modelling using the multivariate skew-t-normal distribution

  2013cited by this paper
• Dimension reduction for model-based clustering via mixtures of multivariate $$t$$t-distributions

  2013cited by this paper
• A mixture of common skew‐t factor analysers

  2013cited by this paper
• Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures

  2013cited by this paper
• Author manuscript, published in "Statistics and Computing (2013)" Statistics and Computing manuscript No. (will be inserted by the editor) A New Family of Multivariate Heavy-tailed Distributions with Variable Marginal Amounts of Tailweight: Application to

  2013cited by this paper
• Dimension reduction for model-based clustering via mixtures of shifted asymmetric Laplace distributions

  2013cited by this paper
• Mixtures of skew-t factor analyzers

  2013influential reference
• Parsimonious skew mixture models for model-based clustering and classification

  2013cited by this paper
• Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions

  2013cited by this paper
• A mixture of generalized hyperbolic factor analyzers

  2013cited by this paper
• Extending mixtures of factor models using the restricted multivariate skew-normal distribution

  2013cited by this paper
• Model-Based Classification via Mixtures of Multivariate t-Factor Analyzers

  2012cited by this paper
• Mixtures of Shifted AsymmetricLaplace Distributions

  2012cited by this paper
• On mixtures of skew normal and skew $$t$$-distributions

  2012cited by this paper
• Analytic calculations for the EM algorithm for multivariate skew-t mixture models

  2012cited by this paper
• Mixtures of Shifted Asymmetric Laplace Distributions

  2012influential reference
• Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions

  2011cited by this paper
• Model-based classification via mixtures of multivariate t-distributions

  2011cited by this paper
• Extending mixtures of multivariate t-factor analyzers

  2011cited by this paper
• Mixtures of modified t-factor analyzers for model-based clustering, classification, and discriminant

  2011cited by this paper
• On the fitting of mixtures of multivariate skew t-distributions via the EM algorithm

  2011influential reference
• Mixtures of common t-factor analyzers for clustering high-dimensional microarray data

  2011cited by this paper
• Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models

  2010cited by this paper
• Complete Gradient Clustering Algorithm for Features Analysis of X-Ray Images

  2010cited by this paper
• Model-based clustering of microarray expression data via latent Gaussian mixture models

  2010cited by this paper
• Heteroscedastic factor mixture analysis

  2010cited by this paper
• Robust mixture modeling using multivariate skew t distributions

  2010cited by this paper
• Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions.

  2010cited by this paper
• Model-based classification using latent Gaussian mixture models

  2010cited by this paper
• A skew-normal factor model for the analysis of student satisfaction towards university courses

  2010cited by this paper
• Model-based clustering with non-elliptically contoured distributions

  2009cited by this paper
• Parsimonious Gaussian mixture models

  2008cited by this paper
• Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution

  2007cited by this paper
• Robust mixture modeling using the skew t distribution

  2007cited by this paper
• The EM Algorithm for Factor Analyzers:An Extension with Latent Variable

  2006cited by this paper
• On fundamental skew distributions

  2005cited by this paper
• Clustering Visualizations of Multidimensional Data

  2004cited by this paper
• A multivariate skew normal distribution

  2004cited by this paper
• A new class of multivariate skew distributions with applications to bayesian regression models

  2003cited by this paper
• Model-Based Clustering, Discriminant Analysis, and Density Estimation

  2002cited by this paper
• Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics

  2001cited by this paper
• Mixtures of Factor Analyzers

  2000cited by this paper
• The EM Algorithm—an Old Folk‐song Sung to a Fast New Tune

  1997cited by this paper
• Mixture models : theory, geometry, and applications

  1995cited by this paper
• The distribution of the likelihood ratio for mixtures of densities from the one-parameter exponential family

  1994cited by this paper
• Statistical Properties of the Generalized Inverse Gaussian Distribution

  1981cited by this paper
• Self-decomposability of the generalized inverse Gaussian and hyperbolic distributions

  1979cited by this paper
• Estimating the Dimension of a Model

  1978cited by this paper
• Infinite divisibility of the hyperbolic and generalized inverse Gaussian distributions

  1977cited by this paper
• Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper

  1977influential reference
• A COMPUTER PROGRAM FOR THE MAXIMUM LIKELIHOOD ANALYSIS OF TYPES

  1965cited by this paper
• THE POPULATION FREQUENCIES OF SPECIES AND THE ESTIMATION OF POPULATION PARAMETERS

  1953influential reference
• Transactions on Pattern Analysis and Machine Intelligence 1 Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualisation of High-dimensional Data

  year unknowncited by this paper
• III.—A Series Formula for the Roots of Algebraic and Transcendental Equations

  year unknowncited by this paper

• Mixtures of Quantile-Based Factor Analyzers

  2025cites this paper
• Variational Autoencoder With Gamma Mixture for Clustering High-Dimensional Right-Skewed Data

  2025cites this paper
• Fuzzy sets and (fuzzy) random sets in Econometrics and Statistics

  2022cites this paper
• Mixture of multivariate gaussian processes for classification of irregularly sampled satellite image time-series

  2022cites this paper
• Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series

  2022cites this paper
• Robust clustering of multiply censored data via mixtures of t factor analyzers

  2021cites this paper
• On formulations of skew factor models: Skew factors and/or skew errors

  2021cites this paper
• Vine copula mixture models and clustering for non-Gaussian data

  2021cites this paper
• A hierarchical mixture cure model with unobserved heterogeneity for credit risk

  2021cites this paper
• Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions

  2020cites this paper
• Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions

  2020influential citation
• A novel mixture model using the multivariate normal mean-variance mixture of Birnbaum-Saunders distributions and its application to extrasolar planets

  2019cites this paper
• Variable Selection

  2019cites this paper
• Dealing with Difficulties

  2019cites this paper
• Model-Based Clustering and Classification for Data Science

  2019cites this paper
• Other Topics

  2019cites this paper
• High-dimensional Data

  2019cites this paper
• Network data

  2019cites this paper
• Discrete Data Clustering

  2019cites this paper
• Preface

  2019cites this paper
• Non-Gaussian Model-based Clustering

  2019cites this paper
• Model-based Clustering with Covariates

  2019cites this paper
• Model-based Clustering: Basic Ideas

  2019cites this paper
• Semi-supervised Clustering and Classification

  2019cites this paper
• Introduction

  2019cites this paper
• Model-based Classification

  2019cites this paper
• Bibliography

  2019cites this paper
• Model-Based Clustering and Classification for Data Science: With Applications in R

  2019cites this paper
• Mixtures of Factor Analyzers with Fundamental Skew Symmetric Distributions

  2018influential citation
• On formulations of skew factor models: skew errors versus skew factors

  2018influential citation
• Mixtures of restricted skew-t factor analyzers with common factor loadings

  2018cites this paper
• Mixtures of skewed matrix variate bilinear factor analyzers

  2018influential citation
• Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data

  2017cites this paper
• Mixtures of Hidden Truncation Hyperbolic Factor Analyzers

  2017cites this paper
• Subspace clustering with the multivariate-t distribution

  2017cites this paper
• On Fractionally-Supervised Classification: Weight Selection and Extension to the Multivariate t-Distribution

  2017cites this paper
• Special issue on mixture models

  2017cites this paper
• A Mixture of Matrix Variate Bilinear Factor Analyzers

  2017influential citation
• Asymmetric clusters and outliers: Mixtures of multivariate contaminated shifted asymmetric Laplace distributions

  2014cites this paper