4 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…