Gevrekci, Y.Guneri, O. I.Takma, C.Yesilova, A.2025-05-102025-05-1020220367-672210.18805/IJAR.BF-14152-s2.0-85139308433https://doi.org/10.18805/IJAR.BF-1415https://hdl.handle.net/20.500.14720/7888Gevrekci, Yakut/0000-0002-4915-2238; Isci Guneri, Oznur/0000-0003-3677-7121Background: 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. 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<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.eninfo:eu-repo/semantics/openAccessCount ModelsHolsteinOverdispersionStillbirthZero InflationArticle569Q4Q411581163WOS:000901201600017