A Beta-GAM hidden Markov model for proportion time series identifies latent regimes and smooth nonlinear covariate effects via penalized EM estimation and information-criterion state selection.
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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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A Beta-GAM Hidden Markov Model for Proportion Time Series
A Beta-GAM hidden Markov model for proportion time series identifies latent regimes and smooth nonlinear covariate effects via penalized EM estimation and information-criterion state selection.
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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.