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

Falk, Matthew G., O'Leary, Rebecca A., Nayak, Manoj, Collins, Patrick, & Low Choy, Samantha (2015) A Bayesian hurdle model for analysis of an insect resistance monitoring database. Environmental and Ecological Statistics, 22(2), pp. 207-226.

View at publisher


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.

Impact and interest:

2 citations in Scopus
Search Google Scholar™
2 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 72986
Item Type: Journal Article
Refereed: Yes
Keywords: Bayesian modelling, Hurdle model, Pest resistance, Post-harvest storage, Grains biosecurity
DOI: 10.1007/s10651-014-0294-3
ISSN: 1573-3009
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Australian and New Zealand Standard Research Classification > AGRICULTURAL AND VETERINARY SCIENCES (070000) > CROP AND PASTURE PRODUCTION (070300) > Crop and Pasture Protection (Pests Diseases and Weeds) (070308)
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Springer Science+Business Media New York
Deposited On: 22 Jun 2014 23:35
Last Modified: 19 Jun 2015 00:53

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page