Analysis of Plant Protection Studies with Excess Zeros Using Zero-Inflated and Negative Binomial Hurdle Models

Authors : Abdullah YESILOVA, Yılmaz KAYA, Barıs KAKI, İsmail KASAP

Pages : 131-136

View : 23 | Download : 9

Publication Date : 2010-03-31

Article Type : Other Papers

Abstract :In this study, the analysis of data with many zeros for plant protection area was carried out by using the models of Poisson Regression insert ignore into journalissuearticles values(PR);, negative binomial insert ignore into journalissuearticles values(NB); regression, zero-inflated Poisson insert ignore into journalissuearticles values(ZIP); regression, zeroinflated negative binomial insert ignore into journalissuearticles values(ZINB); regression, and negative binomial hurdle insert ignore into journalissuearticles values(NBH); model. As zero-inflated observations are too much in the studies, done in the plant protection area; models considering zero-inflated observations are frequently required. Mites insert ignore into journalissuearticles values(Acari: Tetranychidae; Stigmaeidae);, the basic material of this study, can reach to quite high amounts under convenient temperature insert ignore into journalissuearticles values(18-32°C temperature);. The fact that deviance obtained from PR model together with Pearson Chi-square and deviance goodness of statistics came about quite higher than the value of insert ignore into journalissuearticles values(1); represented that there was an overdispersion in data set. In the selection of appropriate regression model, Akaiki information criteria and Bayesian information criteria were used. At the end of these information criteria, ZINB regression was chosen as the best model. In ZINB model, the effects of Zetzellia mali, temperature, and periods were significant on the total P. ulmi number insert ignore into journalissuearticles values(p<0.01);, while applying insecticide was insignificant insert ignore into journalissuearticles values(p>0.05);.     Key Words : Zero-inflated data; overdispersion; Negative binomial Hurdle model; Zero-inflated models.  
Keywords : Zero inflated data, overdispersion, Negative binomial Hurdle model, Zero inflated models