Variable selection in model-based clustering using multilocus genotype data. We propose a variable selection procedure in model-based clustering using multilocus genotype data. Indeed, it may happen that some loci are not relevant for clustering into statistically different populations. Inferring the number K of clusters and the relevant clustering subset S of loci is seen as a model selection problem. The competing models are compared using penalized maximum likelihood criteria. Under weak assumptions on the penalty function, we prove the consistency of the resulting estimator (K ^ n ,S ^ n ). An associated algorithm named mixture model for genotype data (MixMoGenD) has been implemented using C++ programming language and is available on http://www.math.u-psud.fr/ toussile. To avoid an exhaustive search of the optimum model, we propose a modified Backward-Stepwise algorithm, which enables a better search of the optimum model among all possible cardinalities of S. We present numerical experiments on simulated and real datasets that highlight the interest of our loci selection procedure.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
- Nguyen, Xuanlong: Posterior contraction of the population polytope in finite admixture models (2015)
- Bontemps, Dominique; Toussile, Wilson: Clustering and variable selection for categorical multivariate data (2013)
- Toussile, Wilson; Gassiat, Elisabeth: Variable selection in model-based clustering using multilocus genotype data (2009)