A New Zellner’s g-Prior for Bayesian Model Averaging in Regression Analysis
No Thumbnail Available
Date
2018
Authors
Ogundeji, Rotimi
Adeleke, Ismaila
Okafor, Ray
Journal Title
Journal ISSN
Volume Title
Publisher
Faculty of Science, University of Lagos, Lagos, Nigeria.
Abstract
In regression analysis, one of the main challenges is selecting a single model among competing models when
making inferences. Likewise, the issue of the choice of prior distribution has been delicate in data analysis.
Informative prior distributions related to a natural conjugate prior specification are investigated under a limited
choice of a single scalar hyperparameter called g-prior, which corresponds to the degree of prior uncertainty on
regression coefficients. This research identified a set of 11 candidate default priors (Zellner’s g-priors)
prominent in the Literature and applicable in Bayesian model averaging. Some new sets of g-prior structures
were investigated with a view to proposing an improved g-prior specification for regression coefficients in
Bayesian Model Averaging (BMA) and the predictive performance of these g-priors were compared. Results
obtained include the respective prior distributions, posterior distributions and sampling properties of the
regression parameters, based on the new set of g-prior structures investigated. Also, empirical findings revealed
that the proposed g-prior structure exhibited equally competitive and consistent predictive ability when
compared with identified g-prior structures from the Literature.
Description
Keywords
Bayesian Regression , Model Uncertainty , Normal Linear Model , Predictive Performance , Prior Elicitation , Zellner’s g-Priors
Citation
Ogundeji, R. (2018). A New Zellner’s g-Prior for Bayesian Model Averaging in Regression Analysis. Journal of Scientific Research and Development, 18(1), 51-60.