Abstract The authors conduct an extensive simulation study to substantially contribute to the question how HB prior parameter settings (i.e. the prior variance and the prior degrees of freedom) affect the performance of HB-CBC models. The statistical performance of HB is evaluated under experimentally varying conditions based on six experimental factors using criteria for goodness-of-fit, parameter recovery and predictive accuracy. The results indicate that the prior degrees of freedom play a negligible role as there is not any noticeable impact on the performance of HB when varying that factor. For increasing prior variance levels overfitting problems occur that markedly affect parameter recovery and model fit, and a number of related interaction effects with regard to the settings for the prior variance can be observed both at the upper and lower level of the HB model. Perhaps the most striking finding however is that the predictive performance of HB-CBC is hardly affected by an increase of the prior variance. Many of our findings regarding the parameter settings of the inverse Wishart prior contrast those reported in previously proposed empirical studies.

中文翻译：

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关于HB协方差矩阵预先设置的影响：模拟研究

摘要作者进行了广泛的仿真研究，以实质性地回答以下问题：HB先验参数设置（即先验方差和先验自由度）如何影响HB-CBC模型的性能。使用适合度，参数恢复和预测准确性的标准，在六个实验因素的基础上，根据不同的实验条件，评估了HB的统计性能。结果表明，先前的自由度起着微不足道的作用，因为当改变该因素时，对HB的性能没有任何明显的影响。为了增加先验方差水平，会出现过拟合问题，这些问题会显着影响参数恢复和模型拟合，并且可以在HB模型的上层和下层观察到与先验方差设置相关的许多相关交互作用。然而，也许最惊人的发现是HB-CBC的预测性能几乎不受先验方差增加的影响。我们关于反Wishart先验参数设置的许多发现对比了先前提出的实证研究中所报告的结果。