20 citations · 43 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
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…