1 citations · 1 across the 6 of their papers we have counts for
6 papers
Handling mild outliers and unobserved values in compositional datasets using finite mixtures of mean-parametrised Dirichlet models
Jason Pillay, Andriëtte Bekker, Cristina Tortora +1
Heterogeneous compositional data may be simultaneously affected by missing values and atypical points, posing challenges for both clustering and outlier detection. We develop a mix…
Handling Missingness and Censoring in Dirichlet Mixture Models
Jason Pillay, Andriette Bekker, Cristina Tortora +1
Incomplete compositional data analysis faces a fundamental limitation: likelihood-based methods for compositional models generally require fully observed compositions, making it di…
Handling Missingness and Censoring in Dirichlet Models
J. Pillay, A. Bekker, C. Tortora +1
Likelihood-based inference for compositional data generally requires fully observed compositions, hindering the direct treatment of missing or censored components on the simplex. I…
Sleep pattern profiling using a finite mixture of contaminated multivariate skew-normal distributions on incomplete data
Jason Pillay, Cristina Tortora, Antonio Punzo +1
Medical data often exhibit characteristics that make cluster analysis particularly challenging, such as missing values, outliers, and cluster features like skewness. Typically, suc…
Clustering data with values missing at random using scale mixtures of multivariate skew-normal distributions
Jason Pillay, Cristina Tortora, Antonio Punzo +1
Handling missing data is a major challenge in model-based clustering, especially when the data exhibit skewness and heavy tails. We address this by extending the finite mixture of…
Soft computing for the posterior of a new matrix t graphical network
J. Pillay, A. Bekker, J. T. Ferreira +1
Modelling noisy data in a network context remains an unavoidable obstacle; fortunately, random matrix theory may comprehensively describe network environments effectively. Thus it…