From the 1 of 7 linked papers with an AI index.
7 papers
A New Look at Gaussian Mixtures in the Presence of Missing-at-Random Responses and Covariates
Hung Tong, Antonio Punzo, Cristina Tortora
Missing values present a common challenge in statistical modeling, so handling them properly is an important research direction. Among the various mechanisms that can generate miss…
Handling Missingness and Censoring in Dirichlet Mixture Models
Jason Pillay, Andriette Bekker, Cristina Tortora +1
The paper introduces a likelihood‑based EM algorithm for fitting finite mixtures of Dirichlet distributions to compositional data that contain missing or left‑censored components,…
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…
Modelling and detecting mild and gross anomalies in circular data via double-contaminated models
Antonio Punzo, Andriëtte Bekker, Arno Otto +2
In this paper, we propose a model-based framework to robustify inference for circular data in the presence of anomalous observations, distinguishing between mild and gross anomalie…
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…