We consider the problem of testing for a difference in means between clusters of
observations identified via k-means clustering. In this setting, classical hypothesis tests lead …

Modern scientific studies often collect datasets in the form of tensors. These datasets call for
innovative statistical analysis methods. In particular, there is a pressing need for tensor …

This study examined the trajectories of autistic symptom severity in an inception cohort of
187 children with ASD assessed across four time points from diagnosis to age 10. Trajectory …

Finite mixture modeling is a popular technique for capturing heterogeneity in data. Although
the vast majority of the theory developed in this area up to date deals with vector-valued …

A commonly used tool for estimating the parameters of a mixture model is the Expectation–
Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum …

Finite mixtures of regressions with fixed covariates are a commonly used model-based
clustering methodology to deal with regression data. However, they assume assignment …

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 …

Analysis of matrix-variate data is becoming ever more prevalent in the literature, especially
in the area of clustering and classification. Real data, including real matrix-variate data, are …

This paper introduces a novel two-sample test for a broad class of orthogonally invariant
positive definite symmetric matrix distributions. Our test is the first of its kind, and we derive …

A mixture of multiple scaled generalized hyperbolic distributions (MMSGHDs) is introduced.
Then, a coalesced generalized hyperbolic distribution (CGHD) is developed by joining a …