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Sparse Gaussian graphical mixture model. (English. French summary) Zbl 1358.62054

Summary: This paper considers the problem of networks reconstruction from heterogeneous data using a Gaussian Graphical Mixture Model (GGMM). It is well known that parameter estimation in this context is challenging due to large numbers of variables coupled with the degenerate nature of the likelihood. We propose as a solution a penalized maximum likelihood technique by imposing an \(l_1\) penalty on the precision matrix. Our approach shrinks the parameters thereby resulting in better identifiability and variable selection.

MSC:

62H12 Estimation in multivariate analysis
62J07 Ridge regression; shrinkage estimators (Lasso)
62A09 Graphical methods in statistics
62F10 Point estimation
62H30 Classification and discrimination; cluster analysis (statistical aspects)
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Full Text: Euclid