Routine goodness-of-fit analyses of complex models with intractable likelihoods are hampered by a lack of computationally tractable diagnostic measures with well-understood frequency properties, that is, with a known sampling distribution. This frustrates the ability to assess the extremity of the data relative to fitted simulation models in terms of pre-specified test statistics, an essential requirement for model improvement. Given an Approximate Bayesian Computation setting for a posited model with an intractable likelihood for which it is possible to simulate from them, we present a general and computationally inexpensive Monte Carlo framework for obtaining \(p\)-valuesthat are asymptotically uniformly distributed in [0, 1] under the posited model when assumptions about the asymptotic equivalence between the conditional statistic and the maximum likelihood estimator hold. The proposed framework follows almost directly from the conditional predictive p-value proposed in the Bayesian literature. Numerical investigations demonstrate favorable power properties in detecting actual model discrepancies relative to other diagnostic approaches. We illustrate the technique on analytically tractable examples and on a complex tuberculosis transmission model.

缺乏复杂的复杂模型的常规拟合优度分析由于缺乏具有易于理解的频率特性（即已知的采样分布）的易计算的诊断措施而受阻。这阻碍了根据预先指定的测试统计数据评估相对于拟合的仿真模型的数据末端的能力，这是模型改进的基本要求。给定一个近似模型的近似贝叶斯计算设置，并且有可能用它们模拟的难解的可能性，我们提出了一个通用的，计算上不昂贵的蒙特卡洛框架来获得\（p \）当关于条件统计量和最大似然估计量之间的渐近等价的假设成立时，在假定模型下在[0，1]中渐近均匀分布的-值。所提出的框架几乎直接遵循贝叶斯文献中提出的条件预测p值。数值研究表明，相对于其他诊断方法，在检测实际模型差异时具有良好的功率特性。我们在易于分析的实例和复杂的结核病传播模型上说明了该技术。