Page 25 - Contributed Paper Session (CPS) - Volume 1

CPS653 Chang-Yun L.
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Effect

Figure 1: Marginal posterior probabilities of the effects being active for the SPDS design.

large enough. Different from the existing Bayesian methods for multi-
stratum designs, the proposed method selects more reasonable models
that follow the effect heredity principle. To make the proposed method
easy to use, we conduct the MCMC and Gibbs sampling with the WinBUGS
software and provide a general code that can be applied to analyze data
for any SPDS and BDS designs. Applying the SSVS method, we reanalyze
two datasets from the 17 x 8 SPDS design in Lin and Yang (2015) and the
15 x 4 BDS design in Jones and Nachtsheim (2016). To compare the
performance of the SSVS method and the regression methods MSR and
FSR, we conduct simulation studies for SPDS designs with strong effect
heredity models and BDS designs with weak effect heredity models. Results
show that the proposed SSVS method well controls the false discovery rate
and has higher power than the two regression methods on identifying
active effects. Although in this paper the SSVS method and the WinBUGS
code are developed under the framework of the SPDS and BDS designs,
they can be easily extended and applied to analyze data for any other multi-
stratum deigns.

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