The aim is to introduce robust estimation in model based clustering, via the Cluster-Weighted approach (CWM). The latter has been shown to be an effective way to provide data modeling and classiﬁcation for multivariate data. It was originally proposed in the context of media technology and recently extended to a quite general setting. Our aim, here, is to reconsider the EM algorithm which provides parameter estimation, by trimming a fraction a of the data and using restrictions on covariance eigenvalues. This approach enables the model to avoid ﬁtting a small localized random pattern in the data rather than a proper underlying cluster structure. To select appropriate bounds for constrained estimation, a data-driven procedure is illustrated, and applied on real data.
Garcia Escudero, L., Gordaliza, A., Greselin, F., Ingrassia, S., Mayo Iscar, A. (2013). A robust approach in cluster weighted modeling. In E.R.a.A.Y. Xuming He (a cura di), Book of Abstract CFE-ERCIM on Computational and Methodological Statistics 2013. Queen Mary, University of London..