PliableBVS is a new Bayesian hierarchical spike-and-slab model for simultaneous selection of high-dimensional main effects and interactions under an asymmetric weak hierarchical constraint, shown to outperform pliable lasso in simulations.
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A novel spatially dependent shrinkage prior for Poisson regression improves region selection and prediction accuracy for count data with spatially correlated covariates.
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PliableBVS: A flexible Bayesian variable selection method for modeling interactions with mandatory modifying variables
PliableBVS is a new Bayesian hierarchical spike-and-slab model for simultaneous selection of high-dimensional main effects and interactions under an asymmetric weak hierarchical constraint, shown to outperform pliable lasso in simulations.
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Bayesian Region Selection and Prediction in Poisson Regression with Spatially Dependent Global-Local Shrinkage Prior
A novel spatially dependent shrinkage prior for Poisson regression improves region selection and prediction accuracy for count data with spatially correlated covariates.