A joint application of trimming and constraints for robustifying the estimation of mixtures of factor analyzers

Mixtures of factor analyzers are a flexible way for modelling multivariate data with local dependences. It is well known that trimming and restrictions are useful tools for getting robust estimators when estimating mixture models. It will be introduced a new robust estimator for estimating a mixture of factor analyzers based in the joint application of these mentioned tools. The asymptotic and robustness properties of the proposed methodology will be shown. A feasible AECM algorithm for this estimator will be provided. There will be shown evidences about the effectiveness of this methodology when it is applied to real and to artificial data sets.