参数与非参数：适应系数
Parametric versus nonparametric: the fitness coefficient
摘要：
本文引入的适应度系数是参数密度估计和非参数密度估计在数据似然范围内竞争的结果。如在几个真实数据集上所示，适应度系数通常与p值一致，但更容易计算和解释。也就是说，适应度系数可以解释为来自参数模型的数据的比例。此外，适应度系数可以用来建立一个半参数的妥协，提高了推断的参数和非参数的方法。从理论的角度来看，健身系数收敛概率为1，如果模型是真的，如果模型是假的。从实践的角度来看，适应度系数在真实和模拟数据集上的实用性得到了说明。
英文摘要：
The fitness coefficient, introduced in this paper, results from a competition between parametric and nonparametric density estimators within the likelihood of the data. As illustrated on several real datasets, the fitness coefficient generally agrees with p-values but is easier to compute and interpret. Namely, the fitness coefficient can be interpreted as the proportion of data coming from the parametric model. Moreover, the fitness coefficient can be used to build a semiparamteric compromise which improves inference over the parametric and nonparametric approaches. From a theoretical perspective, the fitness coefficient is shown to converge in probability to one if the model is true and to zero if the model is false. From a practical perspective, the utility of the fitness coefficient is illustrated on real and simulated datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
