Journal
INDIAN JOURNAL OF ANIMAL RESEARCH
Volume 56, Issue 9, Pages 1158-1163Publisher
AGRICULTURAL RESEARCH COMMUNICATION CENTRE
DOI: 10.18805/IJAR.BF-1415
Keywords
Count models; Holstein; Overdispersion; Stillbirth; Zero inflation
Categories
Funding
- TUBITAK (The Scientific and Technological Research Council of Turkey)
- Ege University Research Institute
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This study compares different count data models for stillbirth data and finds that the negative binomial-logit hurdle regression model is the best model.
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. 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.
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