Environmental and Ecological Statistics

, Volume 22, Issue 2, pp 207–226 | Cite as

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

  • Matthew G. Falk
  • Rebecca O’Leary
  • Manoj Nayak
  • Patrick Collins
  • Samantha Low Choy


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.


Bayesian classification trees Generalized additive models  Hurdle model Insect resistance Phosphine 



The authors would like to thank Dr Clair Alston for the very helpful comments on the manuscript. Drs Falk, Nayak, Low Choy and Collins would also like to acknowledge the support of the Australian Governments Cooperative Research Centres Program.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Matthew G. Falk
    • 1
    • 2
  • Rebecca O’Leary
    • 3
  • Manoj Nayak
    • 2
    • 4
  • Patrick Collins
    • 2
    • 4
  • Samantha Low Choy
    • 1
    • 2
  1. 1.Mathematical SciencesQueensland University of TechnologyBrisbaneAustralia
  2. 2.Plant Biosecurity Cooperative Research CentreBruceAustralia
  3. 3.Department of Agriculture and Food, Western AustraliaSouth PerthAustralia
  4. 4.Department of Agriculture, Fisheries and Forestry, QueenslandBrisbaneAustralia

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