Covariate empowered empirical Bayes (CEEB) is a method in Bayesian statistics that combines the strength of Empirical Bayes (EB) and hierarchical Bayes (HB) approaches. EB assumes a common prior distribution for all observations, while HB assumes a different prior distribution for each observation based on observed covariates. CEEB bridges the gap by incorporating covariates into the EB prior, allowing for more tailored and flexible modeling. This empowers the method to leverage both the predictive power of HB and the computational efficiency of EB, making it a powerful tool for analyzing data with complex structures and covariate effects.
