Journal of Physics Conference Series · 2021 · 31 citations · 20 references
Social SciencesData ScienceEpidemiologyHydrometeorologyStatistical ModelingCount Data ModelsPoisson ProcessesPrecipitationCount Data AnalysisExcess Zeros ModelingPoisson ModelsPoisson Process ModelsPoisson RegressionZero-inflated ModelsZero-inflated RegressionZeros
Counting data, especially those with excess zeros that cause over‑dispersion, are common across many fields and motivate the use of specialized zero‑inflated models. The study aims to compare Poisson, Zero‑Inflated Poisson (ZIP), and Zero‑Altered Poisson (ZAP) regression models on rainfall data to identify the best‑fitting model. The authors applied each of these regression models to the rainfall observations, fitting and evaluating them to determine the most suitable approach. The analysis found that the Zero‑Altered Poisson regression outperformed both the standard Poisson and the Zero‑Inflated Poisson models, making it the best fit for the rainfall data.
Abstract Counting data (including zero counts) appear in a variety of applications, so counting models have become popular in many fields. In statistical fields, count data can be defined as observation types that use only non-negative integer values. Sometimes researchers may Counts more zeros than the expected. You may describe Excess zero as Zero-Inflation, excess zeros cause over-dispersion. So, the objective of this paper is use zero-inflated regression models (Poisson Regression model, Zero-Inflated Poisson (ZIP), and Zero-Altered Poisson (ZAP)) to analyse rainfall data and select the best model that deal with these type of data. It has been shown through the study and practical application that the advantage and quality of the Zero-Altered Poisson Regression (ZAPR) where the Zero-Altered Poisson regression model was the best count data model for our data, Although it is hard to distinguish Zero-Inflated Poisson (ZIP) regression model, it is better than Poisson regression model.
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