Finite mixture regression: a sparse variable selection by model selection for clustering

DOI10.1214/15-EJS1082zbMath1329.62279arXiv1409.1331OpenAlexW2964151515MaRDI QIDQ902208

Emilie Devijver

Publication date: 7 January 2016

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1409.1331

zbMATH Keywords

variable selection\(\ell_{1}\)-regularized methodfinite mixture regressionnon-asymptotic penalized criterion

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (8)

Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models ⋮ Joint rank and variable selection for parsimonious estimation in a high-dimensional finite mixture regression model ⋮ Model-based regression clustering for high-dimensional data: application to functional data ⋮ Inverse regression approach to robust nonlinear high-to-low dimensional mapping ⋮ Regression‐based heterogeneity analysis to identify overlapping subgroup structure in high‐dimensional data ⋮ Clusterwise elastic-net regression based on a combined information criterion ⋮ Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership ⋮ A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models

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