Inall regression models, the day of laying and the three different hosts were seen to have a significant effect on daily egg numbers (p < 0.01).
📌 3 Maddede Bu Makale
🎯
Amaç & Hipotez:As zero-inflated observations occur very often in studies on plant protection, models taking into account zero-inflated observations are frequently required.
📊
Temel Bulgular & Veri:For the data used in this study, 1114 (43.84%) of the 2541 observations were zero.
💡
Sonuç & Katkı:Inall regression models, the day of laying and the three different hosts were seen to have a significant effect on daily egg numbers (p < 0.01).
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Zero-inflated count data · Overdispersion · Zero-inflated models
Abstract
As zero-inflated observations occur very often in studies on plant protection, models taking into account zero-inflated observations are frequently required. Especially, zero-inflated observations occur in large numbers for insects whose post-oviposition period lasts long, or that generally lay their eggs during the first days of the oviposition period. For the data used in this study, 1114 (43.84%) of the 2541 observations were zero. In the selection of an appropriate regression model, zeroinflated negative binomial regression was chosen as the best model. Inall regression models, the day of laying and the three different hosts were seen to have a significant effect on daily egg numbers (p < 0.01).
YEŞİLOVA, A., KAYA, Y., KAYDAN, M. B. (2022). MODELING INSECT-EGG DATA WITH EXCESS ZEROS USING ZERO-INFLATED REGRESSION MODELS. Hacettepe Journal of Mathematics and Statistics, Cilt: 39 - Sayı: 2, 273–282. https://ajindex.com/articles/530655
Agresti, A. Categorical Data Analysis (John and Wiley & Sons, Incorporation, New Jersey, Canada, 1997).
Banik, S. and Kibria, B. M. G. On some discrete models and their comparisons: An empiri- cal comparative study, Proceedings of The 5th Sino-International Symposium on Probability, Statistics, and Quantitative Management KU/FGU/JUFE Taipei, Taiwan, ROC May 17, 41–56, 2008 (ICAQM/CDMS, 2008). B¨ohning, D. Zero-inflated Poisson models and C.A.MAN: A tutorial collection of evidence, Biometrical Journal 40(6), 833–843, 1998. B¨ohning, D., Dietz, E. and Schlattmann, P. The zero-inflated Poisson model and the de- cayed, missing and filled teeth index in dental epidemiology, Journal of Royal Statistical Society A 162, 195–209, 1999.
Cameron, A. C. and Trivedi, P. K. Regression Analysis of Count Data (Cambridge University Press, New York, 1998).
Cheung, Y. B.Zero-inflated models for regression analysis of count data: A study of growth and development, Statistics in Medicine 21, 1461–1469, 2002.
Cox, R. Some remarks on overdispersion, Biometrika 70, 269–274, 1983.
Dalrymple, M. L., Hudson, I. L and Ford, R. P. K. Finite mixture, zero-inflated Poisson and Hurdle models with application to SIDS, Computational Statistics & Data Analysis 41, 491–504, 2003.
Frome, E. D., Kutner, M. H. and Beauchamp, J. J. Regression analysis of Poisson-distributed data, Journal of American Statistical Association 68 (344), 935–940, 1973.
Hilbe, J. M. Negative Binomial Regression (Cambridge, UK, 2007).
Jansakul, N. Fitting a zero-inflated negative binomial model via R, In: Proceedings 20th International Workshop on Statistical Modelling. Sidney, Australia, 277–284, 2005.
Kaydan, M. B. and Kılın¸cer, N. Investigation on egg laying character of Phenacoccus aceris (Signoret) (Coccoidea:Hemiptera: Pseudococcidae) on different host plant species, Ankara University Journal of Agriculture 13 (3), 224–230, 2007.
Khoshgoftaar, T. M., Gao, K. and Szabo, R. M. Comparing software fault predictions of pure and zero-inflated Poisson regression models, International Journal of Systems Science 36(9), 707–715, 2005.
Kibria, B. M. G. Applications of some discrete regression models for count data, Pakistan Journal of Statistics and Operation Research, 2 (1), 1–16, 2006.
Lambert, D. Zero-inflated Poisson regression, with an application to defects in manufac- turing. Technometrics 34(1), 1–13, 1992.
Lawles, J. F. Negative binomial and mixed Poisson regression, The Canadian Journal of Statistcs 15 (3), 209–2