Model Choice and Size Distribution: A Bayequentist Approach
提出一个三步模型选择框架，结合频率主义拟合检验和贝叶斯信息准则，并引入微观基础标准，用于从实证数据中选出合理的规模分布模型，以纳米比亚商业养牛场数据验证其有效性。
Abstract We propose a new three‐step model‐selection framework for size distributions in empirical data. It generalizes a recent frequentist plausibility‐of‐fit analysis (step 1) and combines it with a relative ranking based on the Bayesian Akaike information criterion (step 2). We enhance these statistical criteria with the additional criterion of microfoundation (step 3), which is to select the size distribution that comes with a dynamic micromodel of size dynamics. A numerical performance test of step 1 shows that our generalization is able to correctly rule out the distribution hypotheses unjustified by the data at hand. We then illustrate our approach and demonstrate its usefulness with a sample of commercial cattle farms in Namibia. In conclusion, the framework proposed here has the potential to reconcile the ongoing debate about size distribution models in empirical data, the two most prominent of which are the Pareto and the log‐normal distribution.