The aim of this study was to apply for zero-inflated generalized Poisson regression in the modelling of mite counts that include excess values of zero and overdispersion. The results of, as mean regression, overdispersion and zero-inflated regression, were determined in three stages. It was obtained that 33.33% (120 observations) of the total numbers of mite taken as a dependent variable to model had zero values. The overdispersion parameter range was detected to be quite high. It was determined that zero-inflated data and overdispersion had an important effect on mite counts (P&lt; 0.01). The effects of region, month, year, varieties, temperature and humidity were found to be statistically significant on mite counts (P&lt; 0.01). The number of eggs found in harmful mites (Aculus schlechtendali) in the Starking variety was relatively higher than in the Golden variety. The results displayed that the differences among regions and varieties regarding the number of eggs found in harmful mites were statistically significant (P&lt;0.01). &nbsp; &nbsp;

Modeling the mite counts having overdispersion and excess values of&nbsp;zero using zero-inflated generalized poisson regression

<div>Background: The objective of this study is comparing different count data models for stillbirth data. In modeling this type of data, Poisson regression or alternative models can be preferred. 
 
Methods: The poisson, negative binomial, zero-inflated poisson, zero-inflated negative binomial, poisson-logit hurdle and negative binomial-logit hurdle regressions were compared and used to examine the effects of the gender, parity and herd-year-season independent variables on stillbirth. Furthermore, the Log-Likelihood statistics, Akaike Information Criteria, Bayesian Information Criteria and rootogram graphs were used as comparison criteria for performance of the models. According to these criteria, Negative Binomial-Logit Hurdle Regression model was chosen as the best model.&nbsp; 
 
Result: The parameter estimates obtained by Negative Binomial-Logit Hurdle Regression model in relation to the effects of the gender, parity and herd-year-season independent variables on stillbirth were found to be significant (p&lt;0.01). It was found that while stillbirth incidence was higher in males than females, it was found to decrease as the parity increased. As a result, the Negative Binomial Logit Hurdle model was found the best model for stillbirth count data with overdispersion.</div>

Modeling the mite counts having overdispersion and excess values of zero using zero-inflated generalized poisson regression

Comparison of Different Count Models for Investigation of Some Environmental Factors Affecting Stillbirth in Holsteins