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Stability of the posterior mean in linear models An admissibility property of D-optimum and E-optimum designs

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  • Meczarski, Marek
  • Zielinski, Ryszard

Abstract

Stability (robustness) of the Bayes estimates in linear models is considered. As measures of stability, the volume and the diameter of the ellipsoid of the posterior mean are proposed. The classes of priors of interest are sets of normal probability distributions with varying covariance matrices. The admissibility property of D-optimum and E-optimum designs under an optimality concept generated by the robustness idea is stated.

Suggested Citation

  • Meczarski, Marek & Zielinski, Ryszard, 1997. "Stability of the posterior mean in linear models An admissibility property of D-optimum and E-optimum designs," Statistics & Probability Letters, Elsevier, vol. 33(2), pages 117-123, April.
    • Handle: RePEc:eee:stapro:v:33:y:1997:i:2:p:117-123
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    References listed on IDEAS

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    1. Leamer, Edward E, 1982. "Sets of Posterior Means with Bounded Variance Priors," Econometrica, Econometric Society, vol. 50(3), pages 725-736, May.
    2. Potzelberger, Klaus & Polasek, Wolfgang, 1991. "Robust HPD Regions in Bayesian Regression Models," Econometrica, Econometric Society, vol. 59(6), pages 1581-1589, November.
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