Indian Journal of Agricultural Research

  • Chief EditorT. Mohapatra

  • Print ISSN 0367-8245

  • Online ISSN 0976-058X

  • NAAS Rating 5.60

  • SJR 0.293

Frequency :
Bi-monthly (February, April, June, August, October and December)
Indexing Services :
BIOSIS Preview, ISI Citation Index, Biological Abstracts, Elsevier (Scopus and Embase), AGRICOLA, Google Scholar, CrossRef, CAB Abstracting Journals, Chemical Abstracts, Indian Science Abstracts, EBSCO Indexing Services, Index Copernicus
Indian Journal of Agricultural Research, volume 50 issue 6 (december 2016) : 561-566

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

Abdullah Yesilova*1, Baris Kaki2
1<p>Biometry and Genetics Unit, Faculty of Agriculture,&nbsp;University of Yuzuncu Yil, 65080 Van, Turkey.</p>
Cite article:- Yesilova*1 Abdullah, Kaki2 Baris (2016). Modeling the mite counts having overdispersion and excess values ofzero using zero-inflated generalized poisson regression . Indian Journal of Agricultural Research. 50(6): 561-566. doi: 10.18805/ijare.v50i6.6674.

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< 0.01). The effects of region, month, year, varieties, temperature and humidity were found to be statistically significant on mite counts (P< 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<0.01).    

  1. Chen, X. D., Ying-Zi Fu, Y. Z. and Wang, X. R. (2013). Local influence measure of zero-inflated generalized Poisson mixture regression models. Statistics in Medicine. 32: 1294-1312. 

  2. Czado, C., Erhardt, V., Min, A. and Wagner, S. (2007). Dispersion and zero-inflation level applied to patent outsourcing rates Zero-inflated generalized Poisson models with regression effects on the mean. Statistical Modelling.7: 125-153.

  3. Famoye, F. and Singh, K. P. (2003). On inûated generalized poisson regression models. Advanced Applied Statistics. 3: 145–158.

  4. Famoye, F. and Karan, P. S. (2006). Zero- inflated generalized poisson regression model with an application to domestic violence data. Journal of Data Science. 5: 117-130.

  5. Kasap, I. (2010). Seasonal Population Development of Spider Mites (Acari: Tetranychidae) and Their Predators in Sprayed and Unsprayed Apple Orchards in Van, Turkey. XIII International Congress of Acarology | Recife, Pernambuco, Brazil – August 23-27, 2010.

  6. Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics. 34: 1-13. 

  7. Rose, C. E., Martin, S. W., Wannemuehler, K. A. and Plikaytis, B. D. (2006). On the of Zero-inflated and Hurdle Models for Medelling Vaccine Adverse event Count Data. Journal of Biopharmaceutical Statistics. 16: 463-481 

  8. Ser, G. (2012). Determination of appropriate covariance structures in random slope and intercept model applied in repeated measures. Journal of Animal and Plant Sciences. 22: 552-555 

  9. Yesilova, A., Kaydan, B. and Kaya, Y. (2010). Modelling insect-egg data with excess zeros using zero-inflated regression models. Hacettepe Journal of Mathematics and Statistics, 39: 273-282. 

  10. Zamani, H. and Ismail, N. (2014). Functional form for the zero-inflated generalized Poisson regression model. Communication in Statistics-Theory and Methods. 43: 515-529.

  11. Zhao, W., Zhang, R., Liu, J. and Lv, Y. (2014). Semi varying coefficient zero-inflated generalized Poisson regression model. Communication in Statistics-Theory and Methods. 44: 171-185. 

Editorial Board

View all (0)