ContaminatedMixt

ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions. We introduce the R package ContaminatedMixt, conceived to disseminate the use of mixtures of multivariate contaminated normal distributions as a tool for robust clustering and classification under the common assumption of elliptically contoured groups. Thirteen variants of the model are also implemented to introduce parsimony. The expectation-conditional maximization algorithm is adopted to obtain maximum likelihood parameter estimates, and likelihood-based model selection criteria are used to select the model and the number of groups. Parallel computation can be used on multicore PCs and computer clusters, when several models have to be fitted. Differently from the more popular mixtures of multivariate normal and t distributions, this approach also allows for automatic detection of mild outliers via the maximum a posteriori probabilities procedure. To exemplify the use of the package, applications to artificial and real data are presented.


References in zbMATH (referenced in 11 articles )

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  1. Bagnato, Luca; Punzo, Antonio: Unconstrained representation of orthogonal matrices with application to common principal components (2021)
  2. Punzo, Antonio; Tortora, Cristina: Multiple scaled contaminated normal distribution and its application in clustering (2021)
  3. García-Escudero, Luis Angel; Mayo-Iscar, Agustín; Riani, Marco: Model-based clustering with determinant-and-shape constraint (2020)
  4. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  5. Mazza, Angelo; Punzo, Antonio: Mixtures of multivariate contaminated normal regression models (2020)
  6. Yang, Yu-Chen; Lin, Tsung-I; Castro, Luis M.; Wang, Wan-Lun: Extending finite mixtures of (t) linear mixed-effects models with concomitant covariates (2020)
  7. Dotto, Francesco; Farcomeni, Alessio: Robust inference for parsimonious model-based clustering (2019)
  8. Morris, Katherine; Punzo, Antonio; McNicholas, Paul D.; Browne, Ryan P.: Asymmetric clusters and outliers: mixtures of multivariate contaminated shifted asymmetric Laplace distributions (2019)
  9. Angelo Mazza; Antonio Punzo; Salvatore Ingrassia: flexCWM: A Flexible Framework for Cluster-Weighted Models (2018) not zbMATH
  10. Punzo, Antonio; McNicholas, Paul. D.: Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model (2017)
  11. Antonio Punzo, Angelo Mazza, Paul D. McNicholas: ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions (2016) arXiv