PEPBVS-package: Bayesian variable selection using power-expected-posterior...

In PEPBVS: Bayesian Variable Selection using Power-Expected-Posterior Prior

Bayesian variable selection using power–expected–posterior prior

Description

Performs Bayesian variable selection under normal linear models for the data with the model parameters following as prior distributions either the PEP or the intrinsic (a special case of the former). The prior distribution on model space is the uniform over all models or the uniform on model dimension (a special case of the beta–binomial prior). Posterior model probabilities and marginal likelihoods can be derived in closed–form expressions under this setup. The selection is performed by either implementing a full enumeration and evaluation of all possible models (for model spaces of small–to–moderate dimension) or using the MC3 algorithm (for model spaces of large dimension). Complementary functions for hypothesis testing, estimation and predictions under Bayesian model averaging, as well as plotting and printing the results are also available. Selected models can be compared to those arising from other well–known priors.

Details

_PACKAGE

References

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Fouskakis, D. and Ntzoufras, I. (2022) Power–Expected–Posterior Priors as Mixtures of g–Priors in Normal Linear Models. Bayesian Analysis, 17(4): 1073-1099. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/21-BA1288")}

Fouskakis, D. and Ntzoufras, I. (2020) Bayesian Model Averaging Using Power–Expected–Posterior Priors. Econometrics, 8(2): 17. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/econometrics8020017")}

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PEPBVS documentation built on April 3, 2025, 6:12 p.m.