20 citations · 43 across the 13 of their papers we have counts for
15 papers
Robust fuzzy clustering with cellwise outliers
Giorgia Zaccaria, Lorenzo Benzakour, Luis A. García-Escudero +2
In a data matrix, we may distinguish between cases, each represented by a row vector for a statistical unit, and cells, which correspond to single entries of the data matrix. Recen…
Cellwise outlier detection in heterogeneous populations
Giorgia Zaccaria, Luis A. García-Escudero, Francesca Greselin +1
Real-world applications may be affected by outlying values. In the model-based clustering literature, several methodologies have been proposed to detect units that deviate from the…
Measuring income inequality via percentile relativities
Vytaras Brazauskas, Francesca Greselin, Ricardas Zitikis
"The rich are getting richer" implies that the population income distributions are getting more right skewed and heavily tailed. For such distributions, the mean is not the best me…
A text analysis for Operational Risk loss descriptions
Davide Di Vincenzo, Francesca Greselin, Fabio Piacenza +1
Financial institutions manage operational risk (OpRisk) by carrying out activities required by regulation, such as collecting loss data, calculating capital requirements, and repor…
Robust variable selection in the framework of classification with label noise and outliers: applications to spectroscopic data in agri-food
Andrea Cappozzo, Ludovic Duponchel, Francesca Greselin +1
Classification of high-dimensional spectroscopic data is a common task in analytical chemistry. Well-established procedures like support vector machines (SVMs) and partial least sq…
Robust variable selection for model-based learning in presence of adulteration
Andrea Cappozzo, Francesca Greselin, Thomas Brendan Murphy
The problem of identifying the most discriminating features when performing supervised learning has been extensively investigated. In particular, several methods for variable selec…
The Social Welfare Implications of the Zenga Index
Francesca Greselin, Simone Pellegrino, Achille Vernizzi
We introduce the social welfare implications of the Zenga index, a recently proposed index of inequality. Our proposal is derived by following the seminal book by Son (2011) and th…
A Two-Stage Bayesian Semiparametric Model for Novelty Detection with Robust Prior Information
Francesco Denti, Andrea Cappozzo, Francesca Greselin
Novelty detection methods aim at partitioning the test units into already observed and previously unseen patterns. However, two significant issues arise: there may be considerable…
Anomaly and Novelty detection for robust semi-supervised learning
Andrea Cappozzo, Francesca Greselin, Thomas Brendan Murphy
Three important issues are often encountered in Supervised and Semi-Supervised Classification: class-memberships are unreliable for some training units (label noise), a proportion…
A robust approach to model-based classification based on trimming and constraints
Andrea Cappozzo, Francesca Greselin, Thomas Brendan Murphy
In a standard classification framework a set of trustworthy learning data are employed to build a decision rule, with the final aim of classifying unlabelled units belonging to the…
Inferential results for a new measure of inequality
Youri Davydov, Francesca Greselin
In this paper we derive inferential results for a new index of inequality, specifically defined for capturing significant changes observed both in the left and in the right tail of…
Measuring economic inequality and risk: a unifying approach based on personal gambles, societal preferences and references
Francesca Greselin, Ricardas Zitikis
The underlying idea behind the construction of indices of economic inequality is based on measuring deviations of various portions of low incomes from certain references or benchma…
Robust estimation for mixtures of Gaussian factor analyzers, based on trimming and constraints
L. A. García-Escudero, A. Gordaliza, F. Greselin +2
Mixtures of Gaussian factors are powerful tools for modeling an unobserved heterogeneous population, offering - at the same time - dimension reduction and model-based clustering. U…
Robust estimation of mixtures of regressions with random covariates, via trimming and constraints
L. A. Garcia-Escudero, A. Gordaliza, F. Greselin +2
A robust estimator for a wide family of mixtures of linear regression is presented. Robustness is based on the joint adoption of the Cluster Weighted Model and of an estimator base…
Maximum likelihood estimation in constrained parameter spaces for mixtures of factor analyzers
Francesca Greselin, Salvatore Ingrassia
Mixtures of factor analyzers are becoming more and more popular in the area of model based clustering of high-dimensional data. According to the likelihood approach in data modelin…