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Robust inference for parsimonious model-based clustering

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ABSTRACT We introduce a robust clustering procedure for parsimonious model-based clustering. The classical mclust framework is robustified through impartial trimming and eigenvalue-ratio constraints (the tclust framework, which is robust but… Click to show full abstract

ABSTRACT We introduce a robust clustering procedure for parsimonious model-based clustering. The classical mclust framework is robustified through impartial trimming and eigenvalue-ratio constraints (the tclust framework, which is robust but not affine invariant). An advantage of our resulting mtclust approach is that eigenvalue-ratio constraints are not needed for certain model formulations, leading to affine invariant robust parsimonious clustering. We illustrate the approach via simulations and a benchmark real data example. R code for the proposed method is available at https://github.com/afarcome/mtclust.

Keywords: model based; parsimonious model; robust inference; model; based clustering; inference parsimonious

Journal Title: Journal of Statistical Computation and Simulation
Year Published: 2018

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