Abstract
We consider a finite mixture of Gaussian regression models for high-dimensional data, where the number of covariates may be much larger than the sample size. We propose to estimate the unknown conditional mixture density by a maximum likelihood estimator, restricted on relevant variables selected by an
Citation
Emilie Devijver. "Finite mixture regression: A sparse variable selection by model selection for clustering." Electron. J. Statist. 9 (2) 2642 - 2674, 2015. https://doi.org/10.1214/15-EJS1082
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