Abstract
The Power-Expected-Posterior (PEP) prior gives 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 (EPP) and furthermore it drops the need of selection over the imaginary data and decreases their effect over the final prior. Under the PEP prior methodology an initial (usually default) baseline prior is updated using imaginary data. This work focuses on normal regression models when the number of observations n is smaller than the number of explanatory variables p. We introduce the PEP prior methodology using different baseline shrinkage priors and we perform some comparisons in simulated data sets.
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This work has received funding from the Research Program PEVE 2020 of the National Technical University of Athens.
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Tzoumerkas, G., Fouskakis, D. (2022). Power-Expected-Posterior Methodology with Baseline Shrinkage Priors. In: Argiento, R., Camerlenghi, F., Paganin, S. (eds) New Frontiers in Bayesian Statistics. BAYSM 2021. Springer Proceedings in Mathematics & Statistics, vol 405. Springer, Cham. https://doi.org/10.1007/978-3-031-16427-9_4
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DOI: https://doi.org/10.1007/978-3-031-16427-9_4
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