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Empirical Bayes Model Averaging in the Presence of Model Misfit

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2016, Doctor of Philosophy, Ohio State University, Statistics.
We study the performance of Bayesian Model Averaging (BMA) with respect to out-of-sample prediction accuracy in the presence of model misfits. Zellner’s g prior remains popular in Bayesian regression modeling, and treatment of the parameters in the prior is directly connected to the quality of posterior inference and prediction. We investigate methods of empirical estimation of the tuning parameters in the prior that attempt to attenuate the impact of model misfit on prediction accuracy. Initially, we focus on optimal prediction under the g prior in the presence of influential cases. Next, we develop several methods that aim to increase the robustness of BMA through adjusting the g priors. Then, we show through simulation that out-of-sample predictive performance can often be improved by averaging predictions across a variety of methodologies. Finally, we extend our idea to other situations of model misfit.
Mario Peruggia (Advisor)
Christopher Hans (Advisor)
Steven MacEachern (Committee Member)
Xu Xinyi (Committee Member)
166 p.

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Citations

  • Wang, J. (2016). Empirical Bayes Model Averaging in the Presence of Model Misfit [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1469437723

    APA Style (7th edition)

  • Wang, Junyan. Empirical Bayes Model Averaging in the Presence of Model Misfit. 2016. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1469437723.

    MLA Style (8th edition)

  • Wang, Junyan. "Empirical Bayes Model Averaging in the Presence of Model Misfit." Doctoral dissertation, Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1469437723

    Chicago Manual of Style (17th edition)