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

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

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

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1206
Author(s):  
Guoxin Zuo ◽  
Kang Fu ◽  
Xianhua Dai ◽  
Liwei Zhang
Keyword(s):  
Count Data ◽  
Generalized Method Of Moments ◽  
Poisson Model ◽  
Hurdle Model ◽  
Generalized Poisson ◽  
South Wales ◽  
Health Research Council ◽  
Using Data ◽  
Generalized Poisson Model ◽  
Better Than

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.

scholarly journals Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 282
Author(s):  
Mabel Morales-Otero ◽  
Vicente Núñez-Antón
Keyword(s):  
Infant Mortality ◽  
Count Data ◽  
Spatial Dependence ◽  
Poisson Model ◽  
Mortality Rates ◽  
Estimation Methods ◽  
Neighborhood Structure ◽  
Specific Context ◽  
Count Data Models ◽  
Conditional Mean

In this paper, we review overdispersed Bayesian generalized spatial conditional count data models. Their usefulness is illustrated with their application to infant mortality rates from Colombian regions and by comparing them with the widely used Besag–York–Mollié (BYM) models. These overdispersed models assume that excess of dispersion in the data may be partially caused from the possible spatial dependence existing among the different spatial units. Thus, specific regression structures are then proposed both for the conditional mean and for the dispersion parameter in the models, including covariates, as well as an assumed spatial neighborhood structure. We focus on the case of response variables following a Poisson distribution, specifically concentrating on the spatial generalized conditional normal overdispersion Poisson model. Models were fitted by making use of the Markov Chain Monte Carlo (MCMC) and Integrated Nested Laplace Approximation (INLA) algorithms in the specific context of Bayesian estimation methods.

scholarly journals Count data models for demographic data∗

1994 ◽  
Vol 4 (3) ◽  
pp. 205-221 ◽  
Author(s):  
Rainer Winkelmann ◽  
Klaus F. Zimmermann
Keyword(s):  
Count Data ◽  
Demographic Data ◽  
Data Models ◽  
Count Data Models

scholarly journals Analysis of Road Traffic Accidents in the Punjab by Using Panel Count Data Models

2021 ◽  
Vol 3 (1) ◽  
pp. 1-13
Author(s):  
Muhammad Anus Hayat Khan ◽  
Ijaz Hussain
Keyword(s):  
Count Data ◽  
Data Model ◽  
Road Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Data Models ◽  
Panel Count Data ◽  
Road Traffic Accidents ◽  
Count Data Models ◽  
Count Data Model

Each year more than three thousand people die and get serious injuries in traffic accidents. Count data model provide more precise tools for planners and decision makers to conduct proactive road safety planning.We analyzed the exploratory research of Road Traffic Accidents (RTAs) and furthermore explores the factors affecting the RTAs frequency in 36 districts of the Punjab over a time period of three years (July 1, 2013 June 30, 2016) with monthly data using panel count data models. Among the models considered, the random parameters Poisson panel count data model is found to fit the data best. The exploratory analysis shows that highly dense populated districts with large number of registered vehicles causes more accidents as compared to low density populated districts. It is found that, most of the variables used to control the variation in the frequency of RTAs counts play vital role with higher significance levels. The application of regression analysis and modeling of RTAs at district level in Punjab will help to identification of districts with high RTAs rates and this could help more efficient road safety management in the Punjab.

Sign in / Sign up

Export Citation Format

Share Document