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Quantifying Wildlife Abundance: Negative Rayleigh Modeling of Line Transect Data

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  • Abdullah M. Almarashi

    (Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

This study introduces a negative Rayleigh detection model for estimating population abundance in line transect surveys. The model satisfies key detection conditions and provides a detailed analysis of its probability density function, moments, and other essential characteristics. Parameters are estimated using three methods: moment estimator, maximum likelihood estimator, and Bayesian estimator. The model’s performance is evaluated through simulations, comparing its estimators to those from established models. An empirical application using perpendicular distance data further assesses the model, with goodness-of-fit statistics demonstrating its advantages over traditional methods.

Suggested Citation

  • Abdullah M. Almarashi, 2024. "Quantifying Wildlife Abundance: Negative Rayleigh Modeling of Line Transect Data," Mathematics, MDPI, vol. 12(17), pages 1-19, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2706-:d:1467776
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    References listed on IDEAS

    as
    1. Ghitany, M.E. & Al-Mutairi, D.K. & Balakrishnan, N. & Al-Enezi, L.J., 2013. "Power Lindley distribution and associated inference," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 20-33.
    2. Hassan S. Bakouch & Christophe Chesneau & Rawda I. Abdullah, 2022. "A pliant parametric detection model for line transect data sampling," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(21), pages 7340-7353, November.
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