A semiparametric framework clusters high-dimensional elliptical data with heavy tails via cluster-specific centers, a common unknown radial generator, and a shared sparse precision matrix, with GEM algorithm and high-dimensional consistency guarantees.
Biostatistics , year =
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A Bayesian framework with adaptive elastic nets and variational EM infers Gaussian graphical models from high-dimensional data with reliable FDR control and good power on heterogeneous graphs.
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Semiparametric Elliptical Mixture Clustering for High-Dimensional Data
A semiparametric framework clusters high-dimensional elliptical data with heavy tails via cluster-specific centers, a common unknown radial generator, and a shared sparse precision matrix, with GEM algorithm and high-dimensional consistency guarantees.
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A Bayesian framework with adaptive elastic nets for the inference of Gaussian graphical models
A Bayesian framework with adaptive elastic nets and variational EM infers Gaussian graphical models from high-dimensional data with reliable FDR control and good power on heterogeneous graphs.