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Conditional likelihood approach for analyzing single visit abundance survey data in the presence of zero inflation and detection error

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  • Péter Sólymos
  • Subhash Lele
  • Erin Bayne

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  • Péter Sólymos & Subhash Lele & Erin Bayne, 2012. "Conditional likelihood approach for analyzing single visit abundance survey data in the presence of zero inflation and detection error," Environmetrics, John Wiley & Sons, Ltd., vol. 23(2), pages 197-205, March.
    • Handle: RePEc:wly:envmet:v:23:y:2012:i:2:p:197-205
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    Cited by:

    1. Vasilios Liordos & Jukka Jokimäki & Marja-Liisa Kaisanlahti-Jokimäki & Evangelos Valsamidis & Vasileios J. Kontsiotis, 2021. "Niche Analysis and Conservation of Bird Species Using Urban Core Areas," Sustainability, MDPI, vol. 13(11), pages 1-15, June.
    2. Zhao, Qing & Royle, J. Andrew, 2019. "Dynamic N-mixture models with temporal variability in detection probability," Ecological Modelling, Elsevier, vol. 393(C), pages 20-24.
    3. Matt Higham & Jay Ver Hoef & Lisa Madsen & Andy Aderman, 2021. "Adjusting a finite population block kriging estimator for imperfect detection," Environmetrics, John Wiley & Sons, Ltd., vol. 32(1), February.
    4. Hefley, Trevor J. & Tyre, Andrew J. & Blankenship, Erin E., 2017. "Reprint of: Fitting population growth models in the presence of measurement and detection error," Ecological Modelling, Elsevier, vol. 359(C), pages 461-467.
    5. Hefley, Trevor J. & Tyre, Andrew J. & Blankenship, Erin E., 2013. "Fitting population growth models in the presence of measurement and detection error," Ecological Modelling, Elsevier, vol. 263(C), pages 244-250.
    6. Wen‐Han Hwang & Richard Huggins & Jakub Stoklosa, 2022. "A model for analyzing clustered occurrence data," Biometrics, The International Biometric Society, vol. 78(2), pages 598-611, June.

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