Generalized Poisson Hurdle Model for Count Data and Its Application in Ear Disease

For count data, though a zero-inflated model can work perfectly well with an excess of zeroes and the generalized Poisson model can tackle over- or under-dispersion, most models cannot simultaneously deal with both zero-inflated or zero-deflated data and over- or under-dispersion. Ear diseases are important in healthcare, and falls into this kind of count data. This paper introduces a generalized Poisson Hurdle model that work with count data of both too many/few zeroes and a sample variance not equal to the mean. To estimate parameters, we use the generalized method of moments. In addition, the asymptotic normality and efficiency of these estimators are established. Moreover, this model is applied to ear disease using data gained from the New South Wales Health Research Council in 1990. This model performs better than both the generalized Poisson model and the Hurdle model.

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GEE-based zero-inflated generalized Poisson model for clustered over or under-dispersed count data

Journal of Statistical Computation and Simulation ◽

10.1080/00949655.2019.1632857 ◽

2019 ◽

Vol 89 (14) ◽

pp. 2711-2732 ◽

Cited By ~ 2

Author(s):

Fatemeh Sarvi ◽

Abbas Moghimbeigi ◽

Hossein Mahjub

Keyword(s):

Count Data ◽

Poisson Model ◽

Generalized Poisson ◽

Generalized Poisson Model

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Likelihood-based analysis of longitudinal count data using a generalized Poisson model

Statistical Modelling ◽

10.1191/1471082x03st050oa ◽

2003 ◽

Vol 3 (2) ◽

pp. 99-108 ◽

Cited By ~ 10

Author(s):

Peter J Toscas ◽

Malcolm J Faddy

Keyword(s):

Count Data ◽

Poisson Model ◽

Generalized Poisson ◽

Longitudinal Count Data ◽

Generalized Poisson Model

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Can Generalized Poisson model replace any other count data models? An evaluation

Clinical Epidemiology and Global Health ◽

10.1016/j.cegh.2021.100774 ◽

2021 ◽

pp. 100774

Author(s):

Bijesh Yadav ◽

Lakshmanan Jeyaseelan ◽

Visalakshi Jeyaseelan ◽

Jothilakshmi Durairaj ◽

Sebastian George ◽

...

Keyword(s):

Count Data ◽

Poisson Model ◽

Data Models ◽

Count Data Models ◽

Generalized Poisson ◽

Generalized Poisson Model

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Comparing of Car-Bym, Generalized Poisson dan Negative Binomial on Tuberculosis Data in Banyumas Districs

Indonesian Journal of Statistics and Its Applications ◽

10.29244/ijsa.v5i1p130-140 ◽

2021 ◽

Vol 5 (1) ◽

pp. 130-140

Author(s):

Jajang Jajang ◽

Budi Pratikno ◽

Mashuri Mashuri

Keyword(s):

Parameter Estimation ◽

Maximum Likelihood ◽

Count Data ◽

Degrees Of Freedom ◽

Negative Binomial ◽

Likelihood Estimation ◽

Poisson Model ◽

Generalized Poisson ◽

Central Java ◽

Area Data

In 2019 the number of people with TB (Tuberculosis) in Banyumas, Central Java, is high (1,910 people have been detected with TB). The number of people infected Tuberculosis (TB) in Banyumas is the count data and it is also the area data. In modeling, the parameter estimation and characteristic of the data need to be considered. Here, we studied comparing Generalized Poisson (GP), negative binomial (NB), and Poisson and CAR.BYM model for TB cases in Banyumas. Here, we use two methods for parameter estimation, maximum likelihood estimation (MLE) and Bayes. The MLE is used for GP and NB models, whereas Bayes is used for Poisson and CAR-BYM. The results showed that Poisson model detected overdispersion where deviance value is 67.38 for 22 degrees of freedom. Therefore, ratio of deviance to degrees of freedom is 3.06 (>1). This indicates that there was overdispersion. The folowing GP, NB, Poisson-Bayes and CAR-BYM are used to modeling TB data in Banyumas and we compare their RMSE. With refer to RMES criteria, we found that CAR-BYM is the best model for modeling TB in Banyumas because its RMSE is smallest.

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Migration Models Incorporating Interdependence of Movers

Environment and Planning A Economy and Space ◽

10.1068/a271493 ◽

1995 ◽

Vol 27 (9) ◽

pp. 1493-1502 ◽

Cited By ~ 3

Author(s):

R Flowerdew ◽

P J Boyle

Keyword(s):

Goodness Of Fit ◽

Spatial Interaction ◽

Local Level ◽

Poisson Model ◽

Data Set ◽

Generalized Poisson ◽

Spatial Interaction Models ◽

Generalized Poisson Model ◽

Generalized Cost ◽

Number Of Individuals

Models of migration between regions are often based on the assumption that individual moves can be modelled by a Poisson distribution whose parameter is a function of origin and destination characteristics, and generalized cost; this is true of Poisson regression models and spatial interaction models. The Poisson assumption is that each individual acts independently from others making the same move. In fact, migration is usually engaged in by household groups, not independent individuals, making the Poisson assumption invalid. It is possible instead to construct a model in which the probability of a household moving is given by a Poisson model and the number of individuals in a moving household is given by an observed household-size distribution. This generalized Poisson model is explained and fitted to a set of data on local-level migration within the English county of Hereford and Worcester. However, the sparse nature of the data set raises problems in assessing goodness of fit because the deviance value is unusually low. This is tackled here with a simulation methodology.

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