), we employ stochastic search variable selection (SSVS) to sort through all 234 possible models. Looking across both cross …

matching, we contrast ad hoc variable selection based on the available literature with a data-driven variable selection method … subgroup-specific and may not generalize to the overall population. Methodologically, we find that data-driven variable … selection identifies a refined set of covariates and changes the magnitude of the estimation results substantially. It may thus …

In this paper, we develop a general method for heterogeneous variable selection in Bayesian nonlinear panel data models …. Heterogeneous variable selection refers to the possibility that subsets of units are unaffected by certain variables. It may be …'s unit-specific parameters: each unit-specific parameter is either equal to zero (heterogeneous variable selection) or comes …

variable selection under normal linear models. We derive a BMA point estimate of a predicted value, and present computation and …

multivariate random forest (MVRF) based variable selection method to identify the key predictors of price change. This variable … robustness of the proposed variable selection method against extant benchmarks such as univariate random forest (RF) and LASSO … selection method has potentially wider application to similar high dimensional modeling situations, e.g., in selecting variables …

matching, we contrast ad hoc variable selection based on the available literature with a data-driven variable selection method … subgroup-specific and may not generalize to the overall population. Methodologically, we find that data-driven variable … selection identifies a refined set of covariates and changes the magnitude of the estimation results substantially. It may thus …

variable selection under normal linear models. We derive a BMA point estimate of a predicted value, and present computation and …