Statistical Methods & Applications

, Volume 24, Issue 4, pp 623–649 | Cite as

Cluster-weighted \(t\)-factor analyzers for robust model-based clustering and dimension reduction

  • Sanjeena SubediEmail author
  • Antonio Punzo
  • Salvatore Ingrassia
  • Paul D. McNicholas


Cluster-weighted models represent a convenient approach for model-based clustering, especially when the covariates contribute to defining the cluster-structure of the data. However, applicability may be limited when the number of covariates is high and performance may be affected by noise and outliers. To overcome these problems, common/uncommon \(t\)-factor analyzers for the covariates, and a \(t\)-distribution for the response variable, are here assumed in each mixture component. A family of twenty parsimonious variants of this model is also presented and the alternating expectation-conditional maximization algorithm, for maximum likelihood estimation of the parameters of all models in the family, is described. Artificial and real data show that these models have very good clustering performance and that the algorithm is able to recover the parameters very well.


Cluster-weighted models Factor analyzers Multivariate \(t\)-distributions Parsimonious models 

Supplementary material

10260_2015_298_MOESM1_ESM.pdf (195 kb)
Supplementary material 1 (pdf 195 KB)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Sanjeena Subedi
    • 1
    Email author
  • Antonio Punzo
    • 2
  • Salvatore Ingrassia
    • 2
  • Paul D. McNicholas
    • 3
  1. 1.Department of Mathematics and StatisticsUniversity of GuelphGuelphCanada
  2. 2.Department of Economics and BusinessUniversity of CataniaCataniaItaly
  3. 3.Department of Mathematics and StatisticsMcMaster UniversityHamiltonCanada

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