A goodness-of-fit diagnostic for count data derived from half-normal plots with a simulated envelope

TL;DR

This paper introduces a new graphical goodness-of-fit diagnostic for count data using half-normal plots with simulation envelopes, providing an objective, model-agnostic evaluation tool validated through simulations and case studies.

Contribution

It develops a novel distance metric based on half-normal plots with simulation envelopes for model assessment and selection, applicable beyond likelihood-based models.

Findings

The proposed metric effectively assesses model fit in count data.

Simulation studies demonstrate the method's reliability and robustness.

Case studies illustrate practical applications in ecology and fisheries.

Abstract

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.