Login | Create Account (DAF staff only)

A Bayesian hurdle model for analysis of an insect resistance monitoring database

Falk, M. G. and O'Leary, R. and Nayak, M. and Collins, P. and Low Choy, S. (2014) A Bayesian hurdle model for analysis of an insect resistance monitoring database. Environmental and Ecological Statistics . ISSN 13528505 (ISSN)

Full text not currently attached. Access may be available via the Publisher's website or OpenAccess link.

Article Link(s): http://dx.doi.org/10.1007/s10651-014-0294-3

Abstract

Motivated by the analysis of the Australian Grain Insect Resistance Database (AGIRD), we develop a Bayesian hurdle modelling approach to assess trends in strong resistance of stored grain insects to phosphine over time. The binary response variable from AGIRD indicating presence or absence of strong resistance is characterized by a majority of absence observations and the hurdle model is a two step approach that is useful when analyzing such a binary response dataset. The proposed hurdle model utilizes Bayesian classification trees to firstly identify covariates and covariate levels pertaining to possible presence or absence of strong resistance. Secondly, generalized additive models (GAMs) with spike and slab priors for variable selection are fitted to the subset of the dataset identified from the Bayesian classification tree indicating possibility of presence of strong resistance. From the GAM we assess trends, biosecurity issues and site specific variables influencing the presence of strong resistance using a variable selection approach. The proposed Bayesian hurdle model is compared to its frequentist counterpart, and also to a naive Bayesian approach which fits a GAM to the entire dataset. The Bayesian hurdle model has the benefit of providing a set of good trees for use in the first step and appears to provide enough flexibility to represent the influence of variables on strong resistance compared to the frequentist model, but also captures the subtle changes in the trend that are missed by the frequentist and naive Bayesian models. © 2014 Springer Science+Business Media New York.

Item Type:Article
Business groups:Horticulture and Forestry Science
Additional Information:Correspondence Address: Falk, M. G.; Mathematical Sciences, Queensland University of Technology, George St, Brisbane, QLD, Australia; email: m.falk@qut.edu.au
Keywords:Bayesian classification trees Generalized additive models Hurdle model Insect resistance Phosphine
Subjects:Plant culture > Field crops > Grain. Cereals
Science > Entomology
Deposited On:01 Jul 2014 06:04
Last Modified:01 Jul 2014 06:04

Repository Staff Only: item control page