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A Note on the Royle–Nichols Model for Repeated Detection–Nondetection Data

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  • Linda M. Haines

    (University of Cape Town)

Abstract

In this note, it is shown that the integrated likelihood for the Royle–Nichols model with a Poisson mixing distribution can be expressed as a finite rather than an infinite sum of terms. The advantages which so accrue are discussed and explored by means of two examples. The finite sum formulation of the likelihood is also shown to hold for negative binomial and zero-inflated mixing distributions. Results based on these two mixing distributions proved disappointing however and their use is not recommended unless extensive data are available.

Suggested Citation

  • Linda M. Haines, 2016. "A Note on the Royle–Nichols Model for Repeated Detection–Nondetection Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 588-598, September.
    • Handle: RePEc:spr:jagbes:v:21:y:2016:i:3:d:10.1007_s13253-016-0253-6
    DOI: 10.1007/s13253-016-0253-6
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    References listed on IDEAS

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    1. Linda M. Haines, 2016. "Maximum likelihood estimation for N‐mixture models," Biometrics, The International Biometric Society, vol. 72(4), pages 1235-1245, December.
    2. Fiske, Ian & Chandler, Richard, 2011. "unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i10).
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