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Previous authors have made Bayesian multinomial probit models identifiable by fixing a parameter on the main diagonal of the covariance matrix. The choice of which element one fixes can influence posterior predictions. Thus, we propose restricting the trace of the covariance matrix, which we achieve without computational penalty. This permits a prior that is symmetric to permutations of the nonbase outcome categories. We find in real and simulated consumer choice datasets that the trace-restricted model is less prone to making extreme predictions. Further, the trace restriction can provide stronger identification, yielding marginal posterior distributions that are more easily interpreted. 相似文献
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Strully Kate W. Bozick Robert Huang Ying Burgette Lane F. 《Population research and policy review》2020,39(6):1143-1184
Population Research and Policy Review - In recent decades, several states have enacted their own immigration enforcement policies. This reflects substantial variation in the social environments... 相似文献
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