1 citations · 4 across the 16 of their papers we have counts for
17 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…
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…
A Contaminated Model for Overdispersed Multinomial Microbiome Count Data
Ockert van Heerden, Andriëtte Bekker, Seite Makgai +2
Multinomial count data, such as microbial composition profiles derived from sequencing studies, frequently contain anomalous observations that distort parameter estimates. The Diri…
Mean regression for (0,1) responses via beta scale mixtures
Arno Otto, Andriëtte Bekker, Johan Ferreira +1
To achieve a greater general flexibility for modeling heavy-tailed bounded responses, a beta scale mixture model is proposed. Each member of the family is obtained by multiplying t…
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…
Mixtures of multivariate linear asymmetric Laplace regressions with multiple asymmetric Laplace covariates
Arnoldus F. Otto, Andriëtte Bekker, Antonio Punzo +2
In response to the challenge of accommodating non-Gaussian behaviour in data, the shifted asymmetric Laplace (SAL) cluster-weighted model (SALCWM) is introduced as a model-based me…
Modeling Bounded Count Environmental Data Using a Contaminated Beta-Binomial Regression Model
Arnoldus F. Otto, Antonio Punzo, Johannes T. Ferreira +3
This paper investigates two environmental applications related to climate change, where observations consist of bounded counts. The binomial and beta-binomial (BB) models are commo…
Does wind affect the orientation of vegetation stripes? A copula-based mixture model for axial and circular data
Marco Mingione, Francesco Lagona, Priyanka Nagar +4
Motivated by a case study of vegetation patterns, we introduce a mixture model with concomitant variables to examine the association between the orientation of vegetation stripes a…
Type I multivariate Pólya-Aeppli distributions with applications
Claire Geldenhuys, Rene Ehlers, Andriette Bekker
An extensive body of literature exists that specifically addresses the univariate case of zero-inflated count models. In contrast, research pertaining to multivariate models is not…
A dependent circular-linear model for multivariate biomechanical data: Ilizarov ring fixator study
Priyanka Nagar, Andriette Bekker, Mohammad Arashi +2
Biomechanical and orthopaedic studies frequently encounter complex datasets that encompass both circular and linear variables. In most cases the circular and linear variables are (…
In search of the perfect fit: interpretation, flexible modelling, and the existing generalisations of the normal distribution
Andriette Bekker, Matthias Wagener, Muhammad Arashi
Many generalised distributions exist for modelling data with vastly diverse characteristics. However, very few of these generalisations of the normal distribution have shape parame…
Spatio-temporal insights for wind energy harvesting in South Africa
Matthew de Bie, Janet van Niekerk, Andriette Bekker
Understanding complex spatial dependency structures is a crucial consideration when attempting to build a modeling framework for wind speeds. Ideally, wind speed modeling should be…
Directional Gaussian spatial processes for South African wind data
Jacobus S. Blom, Priyanka Nagar, Andriette Bekker
Accurate wind pattern modelling is crucial for various applications, including renewable energy, agriculture, and climate adaptation. In this paper, we introduce the wrapped Gaussi…
Protein Structure Parameterization via Mobius Distributions on the Torus
Mohammad Arashi, Najmeh Nakhaei Rad, Andriette Bekker +1
Proteins constitute a large group of macromolecules with a multitude of functions for all living organisms. Proteins achieve this by adopting distinct three-dimensional structures…