1 citations · 1 across the 6 of their papers we have counts for
7 papers
Modelling compositional data with structural zero values
Omar Alzeley, Michail Tsagris
Compositional data are positive multivariate data whose sum equals 1. A popular method to analyze such data is via log--ratio transformations, which are however not applicable when…
Non--negative matrix factorization using the \textit{R} package \textsf{nnmf}
Volkan Sevinç, Nikolas Kontemeniotis, Theodoros Perdikis +1
Non--negative matrix factorization (NMF) has become an established dimensionality reduction technique for extracting latent structures from non--negative data and has found widespr…
Model--based clustering for spherical and hyper--spherical data using elliptically symmetric distributions
Theodoros Perdikis, Nader Alharbi, Michail Tsagris
Model--based clustering for directional data data has attracted a lot of interest, but most methods utilize rotationally symmetric distributions. This paper suggests the use of ell…
Scalable approximation of the transformation-free linear simplicial-simplicial regression via constrained iterative reweighted least squares
Michail Tsagris, Omar Alzeley
Simplicia-simplicial regression concerns statistical modeling scenarios in which both the predictors and the responses are vectors constrained to lie on the simplex. \cite{fiksel20…
Simplicial clustering using the --transformation
Michail Tsagris, Nikolaos Kontemeniotis
We introduce two simplicial clustering approaches for compositional data, that are adaptations of the --means and of the Gaussian mixture models algorithms, by employing the …
Directional data analysis using the spherical Cauchy and the Poisson kernel-based distribution
Michail Tsagris, Panagiotis Papastamoulis, Shogo Kato
In 2020, two novel distributions for the analysis of directional data were introduced: the spherical Cauchy distribution and the Poisson kernel-based distribution. This paper provi…
Constrained least squares simplicial-simplicial regression
Michail Tsagris
Simplicial-simplicial regression refers to the regression setting where both the responses and predictor variables lie within the simplex space, i.e. they are compositional. For th…