Traditional methods of model diagnostics may include a plethora of graphical techniques based on residual analysis, as well as formal tests (e.g. Shapiro-Wilk test for normality and Bartlett test for homogeneity of variance). In this paper we derive a new distance metric based on the half-normal plot with a simulation envelope, a graphical model evaluation method, and investigate its properties through simulation studies. The proposed metric can help to assess the fit of a given model, and also act as a model selection criterion by being comparable across models, whether based or not on a true likelihood. More specifically, it quantitatively encompasses the model evaluation principles and removes the subjective bias when closely related models are involved. We validate the technique by means of an extensive simulation study carried out using count data, and illustrate with two case studies in ecology and fisheries research.

翻译：传统模型诊断方法包含大量基于残差分析的图形化技术及形式化检验（如正态性检验的Shapiro-Wilk检验与方差齐性检验的Bartlett检验）。本文基于半正态图及其模拟包络这一图形化模型评估方法，推导出新的距离度量指标，并通过模拟实验研究其性质。所提出的度量指标既可用于评估给定模型的拟合度，又因其跨模型可比性（无论模型是否基于真实似然函数）可作为模型选择准则。具体而言，该指标以量化方式整合模型评估准则，消除相近模型比较中的主观偏差。我们通过基于计数数据的大规模模拟实验验证该方法的有效性，并以生态学与渔业研究中的两个案例进行应用说明。