caa a22 4500 606196536 CHVBK 20210128100924.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s10260-015-0298-7 doi (NATIONALLICENCE)springer-10.1007/s10260-015-0298-7 Cluster-weighted $$t$$ t -factor analyzers for robust model-based clustering and dimension reduction [Elektronische Daten] [Sanjeena Subedi, Antonio Punzo, Salvatore Ingrassia, Paul 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 $$t$$ t -factor analyzers for the covariates, and a $$t$$ 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. Springer-Verlag Berlin Heidelberg, 2015 Cluster-weighted models nationallicence Factor analyzers nationallicence Multivariate $$t$$ t -distributions nationallicence Parsimonious models nationallicence Subedi Sanjeena Department of Mathematics and Statistics, University of Guelph, N1G 2W1, Guelph, ON, Canada aut Punzo Antonio Department of Economics and Business, University of Catania, Catania, Italy aut Ingrassia Salvatore Department of Economics and Business, University of Catania, Catania, Italy aut McNicholas Paul Department of Mathematics and Statistics, McMaster University, L8S 4L8, Hamilton, ON, Canada aut Statistical Methods & Applications Springer Berlin Heidelberg 24/4(2015-11-01), 623-649 1618-2510 24:4<623 2015 24 10260 https://doi.org/10.1007/s10260-015-0298-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10260-015-0298-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Subedi Sanjeena Department of Mathematics and Statistics, University of Guelph, N1G 2W1, Guelph, ON, Canada aut NATIONALLICENCE 700 1- Punzo Antonio Department of Economics and Business, University of Catania, Catania, Italy aut NATIONALLICENCE 700 1- Ingrassia Salvatore Department of Economics and Business, University of Catania, Catania, Italy aut NATIONALLICENCE 700 1- McNicholas Paul Department of Mathematics and Statistics, McMaster University, L8S 4L8, Hamilton, ON, Canada aut NATIONALLICENCE 773 0- Statistical Methods & Applications Springer Berlin Heidelberg 24/4(2015-11-01), 623-649 1618-2510 24:4<623 2015 24 10260