| Abstrakt: |
The Power–Expected–Posterior (PEP) prior framework provides us a convenient and objective method to deal with variable selection problems, under the Bayesian perspective, in regression models. The PEP prior inherits all of the advantages of Expected–Posterior–Prior. Furthermore, it avoids the need of selection of imaginary data and mitigates their effect over the final posterior. Under the PEP prior methodology, an initial (usually default) baseline prior is updated using imaginary data. In this work, focus is given in normal regression models when the number of observations is smaller than the number of explanatory variables. We introduce the PEP prior methodology using different baseline shrinkage priors, we present a computational method, and we perform comparisons in simulated and real-life datasets. |
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