90 citations · 197 across the 35 of their papers we have counts for
35 papers · 1 filter
A Unified Framework for Heterogeneity, Contamination, and Missing Data in Multivariate Regression
Hung Tong, Cristina Tortora, Antonio Punzo
Missing values, atypical observations, and heterogeneity across latent groups are common sources of complexity in regression data. The contaminated Gaussian cluster-weighted model…
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…
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
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…
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…