Bayesian variable selection approaches are more powerful in discriminating among
models regardless of whether these models under investigation are hierarchical or not.
Although Bayesian approaches require complex computation, use of theMarkov Chain
Monte Carlo (MCMC) methods, such as, Gibbs sampler and Metropolis-Hastings algorithm
make computations easier. In this study we investigated the e↵ectiveness
of Bayesian variable selection approaches in comparison to other non-Bayesian or
classical approaches. For this purpose, we compared the performance of Bayesian
versus non-Bayesian variable selection approaches for linear models. Among these
approaches, we studied Conditional Predictive Ordinate (CPO) and Bayes factor.
Among the non-Bayesian or classical approaches, we implemented adjusted R-square,
Akaike Information Criterion (AIC) and Bayes Information Criterion (BIC) for model
selection. We performed a simulation study to examine how Bayesian and non-
Bayesian approaches perform in selecting variables. We also applied these methods
to real data and compared their performances. We observed that for linear models,
Bayesian variable selection approaches perform consistently as that of non-Bayesian
approaches. / Bayesian inference -- Bayesian inference for normally distributed likekilhood -- Model adequacy -- Simulation approach -- Application to wage data. / Department of Mathematical Sciences
| Identifer | oai:union.ndltd.org:BSU/oai:cardinalscholar.bsu.edu:123456789/198141 |
| Date | 03 May 2014 |
| Creators | Rahman, Husneara |
| Contributors | Begum, Munni, 1970- |
| Source Sets | Ball State University |
| Detected Language | English |
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