The Poisson regression model is widely used for count data. This model assumes equidispersion. In practice, equidispersion is seldom reflected in data. However, in reallife data, the variance usually exceeds the mean. This situation is known as overdispersion. Negative binomial distribution and other Poisson mix models are often used to model overdispersion count data. Another extension of the negative binomial distribution in another model for count data is the univariate generalized Waring. In addition, the model developed by Famoye can be used in the analysis of count data. When the count data contains a large number of zeros, it is necessary to use zeroinflated models. In this study, different generalized regression models are emphasized for the analysis of excessive zeros count data. For this purpose, a real data set was analysed with the generalized Poisson model, generalized negative binomial model, generalized negative binomial Famoye, generalized Waring model, and the foregoing zeroinflated models. Loglikelihood, Akaike information criterion, Bayes information criterion, Vuong statistics were used for model comparisons.
Primary Language  English 

Subjects  Mathematics, Applied 
Journal Section  Research Article 
Authors 

Publication Date  June 30, 2021 
Acceptance Date  June 4, 2021 
Published in Issue  Year 2021, Volume , Issue 35 
Bibtex  @research article { jnt902066, journal = {Journal of New Theory}, issn = {21491402}, address = {Mathematics Department, Gaziosmanpasa University 60250 TokatTURKEY.}, publisher = {Tokat Gaziosmanpasa University}, year = {2021}, number = {35}, pages = {48  61}, doi = {10.53570/jnt.902066}, title = {Models for Overdispersion Count Data with Generalized Distribution: An Application to Parasites Intensity}, key = {cite}, author = {İşçi Güneri, Öznur and Durmuş, Burcu} } 
APA  İşçi Güneri, Ö. & Durmuş, B. (2021). Models for Overdispersion Count Data with Generalized Distribution: An Application to Parasites Intensity . Journal of New Theory , (35) , 4861 . DOI: 10.53570/jnt.902066 
MLA  İşçi Güneri, Ö. , Durmuş, B. "Models for Overdispersion Count Data with Generalized Distribution: An Application to Parasites Intensity" . Journal of New Theory (2021 ): 4861 <https://dergipark.org.tr/en/pub/jnt/issue/63272/902066> 
Chicago  İşçi Güneri, Ö. , Durmuş, B. "Models for Overdispersion Count Data with Generalized Distribution: An Application to Parasites Intensity". Journal of New Theory (2021 ): 4861 
RIS  TY  JOUR T1  Models for Overdispersion Count Data with Generalized Distribution: An Application to Parasites Intensity AU  Öznurİşçi Güneri, BurcuDurmuş Y1  2021 PY  2021 N1  doi: 10.53570/jnt.902066 DO  10.53570/jnt.902066 T2  Journal of New Theory JF  Journal JO  JOR SP  48 EP  61 VL  IS  35 SN  21491402 M3  doi: 10.53570/jnt.902066 UR  https://doi.org/10.53570/jnt.902066 Y2  2021 ER  
EndNote  %0 Journal of New Theory Models for Overdispersion Count Data with Generalized Distribution: An Application to Parasites Intensity %A Öznur İşçi Güneri , Burcu Durmuş %T Models for Overdispersion Count Data with Generalized Distribution: An Application to Parasites Intensity %D 2021 %J Journal of New Theory %P 21491402 %V %N 35 %R doi: 10.53570/jnt.902066 %U 10.53570/jnt.902066 
ISNAD  İşçi Güneri, Öznur , Durmuş, Burcu . "Models for Overdispersion Count Data with Generalized Distribution: An Application to Parasites Intensity". Journal of New Theory / 35 (June 2021): 4861 . https://doi.org/10.53570/jnt.902066 
AMA  İşçi Güneri Ö. , Durmuş B. Models for Overdispersion Count Data with Generalized Distribution: An Application to Parasites Intensity. JNT. 2021; (35): 4861. 
Vancouver  İşçi Güneri Ö. , Durmuş B. Models for Overdispersion Count Data with Generalized Distribution: An Application to Parasites Intensity. Journal of New Theory. 2021; (35): 4861. 
IEEE  Ö. İşçi Güneri and B. Durmuş , "Models for Overdispersion Count Data with Generalized Distribution: An Application to Parasites Intensity", Journal of New Theory, no. 35, pp. 4861, Jun. 2021, doi:10.53570/jnt.902066 