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

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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.

Keywords

Poisson Negative binomial Zero-inflated Bootstraps 
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Notes

Acknowledgments

I would like to thank the two referees for their extremely thoughtful and insightful comments and my colleagues at the University of Cape Town for many helpful discussions. I would also like to thank the University of Cape Town and the National Research Foundation (NRF) of South Africa, grant (UID) 85456, for financial support. Any opinion, finding and conclusion or recommendation expressed in this material is that of the author and the NRF does not accept liability in this regard.

Supplementary material

13253_2016_253_MOESM1_ESM.pdf (220 kb)
Supplementary material 1 (pdf 220 KB)

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Copyright information

© International Biometric Society 2016

Authors and Affiliations

  1. 1. Department of Statistical SciencesUniversity of Cape TownRondeboschSouth Africa

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