The handling of overdispersion on Poisson regression model with the generalized Poisson regression model [PDF]
AIP Conference Proceedings, 2021 Regression model is used to model the relationship between predictor variables and response variable. In case that the response variable are Poisson distributed, Poisson regression model can be used to model the relationship. An assumption that must be fulfilled on Poisson distribution is the mean value of data equals to the variance value (or so ...
Dewi Retno Sari Saputro +2 more
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Zero-inflated generalized Poisson models with regression effects on the mean, dispersion and zero-inflation level applied to patent outsourcing rates [PDF]
, 2006 This paper focuses on an extension of zero-inflated generalized Poisson (ZIGP) regression models for count data. We discuss generalized Poisson (GP) models where dispersion is modelled by an additional model parameter.
Erhardt, Vinzenz +2 more
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Testing for zero-modification in count regression models [PDF]
, 2006 Count data often exhibit overdispersion and/or require an adjustment for zero outcomes with respect to a Poisson model. Zero-modified Poisson (ZMP) and zero-modified generalized Poisson (ZMGP) regression models are useful classes of models for such ...
Min, Aleksey, Czado, Claudia
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Axioms, 2022 The Poisson maximum likelihood (PML) is used to estimate the coefficients of the Poisson regression model (PRM). Since the resulting estimators are sensitive to outliers, different studies have provided robust Poisson regression estimators to alleviate ...
Issam Dawoud +3 more
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Modelling Generalized Poisson Regression in the Number of Dengue Hemorrhagic Fever (DHF) in East Nusa Tenggara [PDF]
E3S Web of Conferences, 2020 Regression analysis is an analysis used to model the relationship between the dependent variable (Y) and the independent variable (X). If the dependent variable is a discrete random variable, it is developed using the Poisson regression model.
Prahutama Alan +2 more
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Modelling count data with overdispersion and spatial effects [PDF]
, 2005 In this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. Besides the inclusion of covariates, spatial effects are incorporated and modelled using a proper Gaussian conditional autoregressive prior ...
Gschlößl, Susanne, Czado, Claudia
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D-optimal designs for Poisson regression models [PDF]
, 2008 We consider the problem of finding an optimal design under a Poisson regression model with a log link, any number of independent variables and an additive linear predictor.
Woods, D.C. +3 more
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EM Estimation for Zero- and k-Inflated Poisson Regression Model
Computation, 2021 Count data with excessive zeros are ubiquitous in healthcare, medical, and scientific studies. There are numerous articles that show how to fit Poisson and other models which account for the excessive zeros. However, in many situations, besides zero, the
Monika Arora, N. Rao Chaganty
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PEMODELAN JUMLAH KEMATIAN BAYI DI PROVINSI MALUKU TAHUN 2010 DENGAN MENGGUNAKAN REGRESI POISSON
Barekeng, 2012 Infant mortality is an experienced child death before the age of one year. Regression analysis is a statistical analysis that aims to model the relationship between response variables (Y) with predictor variables (X).
Salmon N. Aulele
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Zero to k Inflated Poisson Regression Models with Applications
Journal of Statistical Theory and Applications (JSTA), 2023 In the count data set, the frequency of some points may occur more than expected under the standard data analysis models. Indeed, in many situations, the frequencies of zero and of some other points tend to be higher than those of the Poisson.
Hadi Saboori, Mahdi Doostparast
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