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 21 articles )

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  1. Amovin-Assagba, Martial; Gannaz, Irène; Jacques, Julien: Outlier detection in multivariate functional data through a contaminated mixture model (2022)
  2. Punzo, Antonio; Bagnato, Luca: Dimension-wise scaled normal mixtures with application to finance and biometry (2022)
  3. Sugasawa, Shonosuke; Kobayashi, Genya: Robust fitting of mixture models using weighted complete estimating equations (2022)
  4. Tomarchio, Salvatore D.; Bagnato, Luca; Punzo, Antonio: Model-based clustering via new parsimonious mixtures of heavy-tailed distributions (2022)
  5. Tong, Hung; Tortora, Cristina: Model-based clustering and outlier detection with missing data (2022)
  6. Bagnato, Luca; Punzo, Antonio: Unconstrained representation of orthogonal matrices with application to common principal components (2021)
  7. Punzo, Antonio; Bagnato, Luca: The multivariate tail-inflated normal distribution and its application in finance (2021)
  8. Punzo, Antonio; Ingrassia, Salvatore; Maruotti, Antonello: Multivariate hidden Markov regression models: random covariates and heavy-tailed distributions (2021)
  9. Punzo, Antonio; Tortora, Cristina: Multiple scaled contaminated normal distribution and its application in clustering (2021)
  10. Farcomeni, Alessio; Punzo, Antonio: Robust model-based clustering with mild and gross outliers (2020)
  11. García-Escudero, Luis Angel; Mayo-Iscar, Agustín; Riani, Marco: Model-based clustering with determinant-and-shape constraint (2020)
  12. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  13. Mazza, Angelo; Punzo, Antonio: Mixtures of multivariate contaminated normal regression models (2020)
  14. 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)
  15. Dotto, Francesco; Farcomeni, Alessio: Robust inference for parsimonious model-based clustering (2019)
  16. Morris, Katherine; Punzo, Antonio; McNicholas, Paul D.; Browne, Ryan P.: Asymmetric clusters and outliers: mixtures of multivariate contaminated shifted asymmetric Laplace distributions (2019)
  17. Punzo, Antonio: A new look at the inverse Gaussian distribution with applications to insurance and economic data (2019)
  18. Angelo Mazza; Antonio Punzo; Salvatore Ingrassia: flexCWM: A Flexible Framework for Cluster-Weighted Models (2018) not zbMATH
  19. Punzo, Antonio; Mazza, Angelo; Maruotti, Antonello: Fitting insurance and economic data with outliers: a flexible approach based on finite mixtures of contaminated gamma distributions (2018)
  20. Punzo, Antonio; McNicholas, Paul. D.: Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model (2017)

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