Two new matrix-variate distributions with application in model-based clustering
Two matrix-variate distributions, both elliptical heavy-tailed generalization of the matrix-
variate normal distribution, are introduced. They belong to the normal scale mixture family …
variate normal distribution, are introduced. They belong to the normal scale mixture family …
Matrix-variate normal mean-variance Birnbaum–Saunders distributions and related mixture models
SD Tomarchio - Computational Statistics, 2024 - Springer
Matrix-variate data analysis has increasingly attracted interest in the statistical literature over
the recent years, especially in the model-based clustering framework. Here, we firstly …
the recent years, especially in the model-based clustering framework. Here, we firstly …
Model-based clustering via skewed matrix-variate cluster-weighted models
MPB Gallaugher, SD Tomarchio… - Journal of Statistical …, 2022 - Taylor & Francis
Cluster-weighted models (CWMs) extend finite mixtures of regressions (FMRs) in order to
allow the distribution of covariates to contribute to the clustering process. In this article, we …
allow the distribution of covariates to contribute to the clustering process. In this article, we …
Visual assessment of matrix‐variate normality
N Počuča, MPB Gallaugher, KM Clark… - Australian & New …, 2023 - Wiley Online Library
In recent years, the analysis of three‐way data has become ever more prevalent in the
literature. It is becoming increasingly common to analyse such data by means of matrix …
literature. It is becoming increasingly common to analyse such data by means of matrix …
Four skewed tensor distributions
With the rise of the" big data" phenomenon in recent years, data is coming in many different
complex forms. One example of this is multi-way data that come in the form of higher-order …
complex forms. One example of this is multi-way data that come in the form of higher-order …
Parsimonious mixtures of matrix variate bilinear factor analyzers
MPB Gallaugher, PD McNicholas - … and data science: Essays in honor of …, 2020 - Springer
Over the years, data have become increasingly higher dimensional, which has prompted an
increased need for dimension reduction techniques. This is perhaps especially true for …
increased need for dimension reduction techniques. This is perhaps especially true for …
Mixtures of regressions using matrix-variate heavy-tailed distributions
SD Tomarchio, MPB Gallaugher - Advances in Data Analysis and …, 2024 - Springer
Finite mixtures of regressions (FMRs) are powerful clustering devices used in many
regression-type analyses. Unfortunately, real data often present atypical observations that …
regression-type analyses. Unfortunately, real data often present atypical observations that …
Parsimonious mixtures for the analysis of tensor-variate data
Real data is taking on more and more complex structures, raising the necessity for more
flexible and parsimonious statistical methodologies. Tensor-variate (or multi-way) structures …
flexible and parsimonious statistical methodologies. Tensor-variate (or multi-way) structures …
Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions
AA Sochaniwsky, MPB Gallaugher, Y Tang… - Journal of …, 2024 - Springer
Robust clustering of high-dimensional data is an important topic because clusters in real
datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based …
datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based …
Least-squares bilinear clustering of three-way data
PC Schoonees, PJF Groenen… - Advances in Data Analysis …, 2022 - Springer
A least-squares bilinear clustering framework for modelling three-way data, where each
observation consists of an ordinary two-way matrix, is introduced. The method combines …
observation consists of an ordinary two-way matrix, is introduced. The method combines …