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Informative g -Priors for Mixed Models

Author

Listed:
  • Yu-Fang Chien

    (Department of Statistics and Actuarial Science, Northern Illinois University, DeKalb, IL 60115, USA)

  • Haiming Zhou

    (Department of Statistics and Actuarial Science, Northern Illinois University, DeKalb, IL 60115, USA
    Current Affiliation: Daiichi Sankyo, Inc., Basking Ridge, NJ 07920, USA.)

  • Timothy Hanson

    (Structural Heart & Aortic, Medtronic, Minneapolis, MN 55432, USA)

  • Theodore Lystig

    (BridgeBio, Palo Alto, CA 94304, USA)

Abstract

Zellner’s objective g -prior has been widely used in linear regression models due to its simple interpretation and computational tractability in evaluating marginal likelihoods. However, the g -prior further allows portioning the prior variability explained by the linear predictor versus that of pure noise. In this paper, we propose a novel yet remarkably simple g -prior specification when a subject matter expert has information on the marginal distribution of the response y i . The approach is extended for use in mixed models with some surprising but intuitive results. Simulation studies are conducted to compare the model fitting under the proposed g -prior with that under other existing priors.

Suggested Citation

  • Yu-Fang Chien & Haiming Zhou & Timothy Hanson & Theodore Lystig, 2023. "Informative g -Priors for Mixed Models," Stats, MDPI, vol. 6(1), pages 1-23, January.
    • Handle: RePEc:gam:jstats:v:6:y:2023:i:1:p:11-191:d:1037928
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    References listed on IDEAS

    as
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