Zero-Inflated Regression Methods for Insecticides

Abstract

The numerical abundance of many species sharing the same ecosystem very different levels of the organism and are in constant change, depending on many factors. Due to the heterogeneous strucspeciese of the life cycles of organisms and abiotic resources in the environment based on census population densities derived from overdispersion (variance is higher than means in Poisson distribution) (Cox, 1983; Cameron and Trivedi, 1998) and a large number of zero values (zero-inflated data) is observed (Yesilova et al, 2011). In such a case, zero-inflated Poisson (ZIP) regression model is a appropriate approach for analyzing a dependent variable having excess zero observations (Lambert, 1992; Bohning, 1998; Bohning et al, 1999; Yau and Lee, 2001; Lee et al, 2001; Khoshgoftaar et al, 2005; Yesilova et al, 2010). Zero-inflation is also likely in data sets, excess zero observations. In such cases, a zeroinflated negative binominal (ZINB) regression model is an alternative method (Ridout et al, 2001; Yau, 2001; Cheung, 2002; Jansakul, 2005; Long and Frese, 2006; Hilbe, 2007; Yesilova et al, 2009; Yesilova et al, 2010). Morever, The Poisson hurdle model and negative binomial hurdle model (Rose and Martin, 2006; Long and Frese, 2006; Hilbe, 2007; Yesilova et al, 2009; Yesilova et al, 2010), and zero-inflated generalized Poisson (ZIGP) model (Consul, 1989, Consul and Famoye, 1992; Czado et al., 2007) are widely used in the analysis of zero-inflated data. In this part, the analysis of data with many zeros for Notonecta viridis Delcourt (Heteroptera: Notonectidae) and Chironomidae species (Diptera) were carried out by means of using the models of Poisson Regression (PR), negative binomial (NB) regression, zero-inflated Poisson (ZIP) regression, zero-inflated negative binomial (ZINB) regression and negative binomial hurdle (NBH) model.