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Statistical inference for olfactometer data

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  • I. Ricard
  • A. C. Davison

Abstract

Summary. Olfactometer experiments are used to determine the effect of odours on the behaviour of organisms such as insects or nematodes, and typically result in data comprising many groups of small overdispersed counts. We develop a non‐homogeneous Markov chain model for data from olfactometer experiments with parasitoid wasps and discuss a relation with the Dirichlet–multinomial distribution. We consider the asymptotic relative efficiencies of three different observation schemes and give an analysis of data intended to shed light on the effect of previous experience of odours in the wasps.

Suggested Citation

  • I. Ricard & A. C. Davison, 2007. "Statistical inference for olfactometer data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(4), pages 479-492, August.
    • Handle: RePEc:bla:jorssc:v:56:y:2007:i:4:p:479-492
    DOI: 10.1111/j.1467-9876.2007.00588.x
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    References listed on IDEAS

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    1. M. J. Faddy & D. M. Smith, 2005. "Modeling the Dependence between the Number of Trials and the Success Probability in Binary Trials," Biometrics, The International Biometric Society, vol. 61(4), pages 1112-1114, December.
    2. Yu, Chang & Zelterman, Daniel, 2002. "Sums of dependent Bernoulli random variables and disease clustering," Statistics & Probability Letters, Elsevier, vol. 57(4), pages 363-373, May.
    3. M. J. Faddy & R. J. Bosch, 2001. "Likelihood-Based Modeling and Analysis of Data Underdispersed Relative to the Poisson Distribution," Biometrics, The International Biometric Society, vol. 57(2), pages 620-624, June.
    4. Martin S. Ridout & Malcolm J. Faddy & Michael G. Solomon, 2006. "Modelling the effects of repellent chemicals on foraging bees," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(1), pages 63-75, January.
    5. M. J. Faddy & J. S. Fenlon, 1999. "Stochastic modelling of the invasion process of nematodes in fly larvae," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(1), pages 31-37.
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