Abstract
Count datasets are traditionally analyzed using the ordinary Poisson distribution. However, said model has its applicability limited, as it can be somewhat restrictive to handling specific data structures. In this case, the need arises for obtaining alternative models that accommodate, for example, overdispersion and zero modification (inflation/deflation at the frequency of zeros). In practical terms, these are the most prevalent structures ruling the nature of discrete phenomena nowadays. Hence, this paper’s primary goal was to jointly address these issues by deriving a fixed-effects regression model based on the hurdle version of the Poisson–Sujatha distribution. In this framework, the zero modification is incorporated by considering that a binary probability model determines which outcomes are zero-valued, and a zero-truncated process is responsible for generating positive observations. Posterior inferences for the model parameters were obtained from a fully Bayesian approach based on the g-prior method. Intensive Monte Carlo simulation studies were performed to assess the Bayesian estimators’ empirical properties, and the obtained results have been discussed. The proposed model was considered for analyzing a real dataset, and its competitiveness regarding some well-established fixed-effects models for count data was evaluated. A sensitivity analysis to detect observations that may impact parameter estimates was performed based on standard divergence measures. The Bayesian p-value and the randomized quantile residuals were considered for the task of model validation.
Highlights
The ordinary Poisson (P ) distribution is often adopted for the analysis of count data, mainly due to its simplicity and having computational implementations available for most of the standard statistical packages
The formal concept behind the information matrix prior is closely related to the unit information prior [54], whose main idea is that the amount of information provided by a prior distribution must be the same as the amount of information contained in a single observation
Intensive Monte Carlo simulation studies were performed, and the obtained results have allowed us to assess the empirical properties of the Bayesian estimators and conclude about the suitability of the adopted methodology to the predefined scenarios
Summary
The ordinary Poisson (P ) distribution is often adopted for the analysis of count data, mainly due to its simplicity and having computational implementations available for most of the standard statistical packages. A Bayesian approach for the zero-inflated Poisson (Z IP ) distribution was considered by [33], and by [34] in a regression framework with fixed-effects. This paper aims to extend the works of [42,43] in the sense of developing a new fixed-effects regression model for count data based on the zero-modified. P S distribution accounts for different levels of overdispersion, its zero-modified version is naturally a robust alternative, as it may accommodate discrepant points that would significantly impact the parameter estimates of the Z MP model. Local influence measures based on some well-known divergences were considered for the task of detecting influential points Model validation metrics such as the Bayesian p-value and the randomized quantile residuals are presented.
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