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Abstract:
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Finite mixture regression models are useful for modeling the relationship between response and predictors arising from different subpopulations. In this talk, we study high-dimensional predictors and high-dimensional response and propose a procedure to cluster observations according to the link between predictors and the response. To reduce the dimension, we propose to use the Lasso estimator, which takes into account the sparsity and a maximum likelihood estimator to reduce the bias. To select the number of components and the sparsity level, we construct a collection of models, varying those two parameters and we select a model among this collection with a non-asymptotic criterion. We apply and evaluate our methods both on simulated and real datasets, to understand how they work in practice.
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