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Bayesian estimation of the number of species from Poisson-Lindley stochastic abundance model using non-informative priors

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Abstract

In this article, we propose a Poisson-Lindley distribution as a stochastic abundance model in which the sample is according to the independent Poisson process. Jeffery’s and Bernardo’s reference priors have been obtaining and proposed the Bayes estimators of the number of species for this model. The proposed Bayes estimators have been compared with the corresponding profile and conditional maximum likelihood estimators for their square root of the risks under squared error loss function (SELF). Jeffery’s and Bernardo’s reference priors have been considered and compared with the Bayesian approach based on biological data.

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For research analysis purpose we used secondary data, and it have been shared in this manuscript.

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We would like to confirm that we did not have any funding organization/institute/university that funded our research.

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Authors and Affiliations

  1. Department of Statistics, Central University of Haryana, Mahendragarh, Haryana, 123031, India

    Anurag Pathak, Manoj Kumar & Sandeep Kumar

  2. Department of Statistics, University of Delhi, New Delhi, Delhi, 110007, India

    Manoj Kumar

  3. Department of Statistics, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India

    Sanjay Kumar Singh & Umesh Singh

  4. Department of Mathematics, Applied Science and Humanities, GLA University, Mathura, Uttar Pradesh, 281406, India

    Sandeep Kumar

Authors
  1. Anurag Pathak
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  2. Manoj Kumar
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  3. Sanjay Kumar Singh
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  4. Umesh Singh
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  5. Sandeep Kumar
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All co-authors are equally contributed in this manuscript.

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Correspondence to Manoj Kumar.

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Appendix

Appendix

See Tables 5 and 6.

Table 5 Simulation table for Poisson-Lindley mixed model for fixed \(\theta =0.5\)
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Table 6 Simulation table for Poisson-Lindley mixed model for fixed value \(\theta =1.2\)
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Pathak, A., Kumar, M., Singh, S.K. et al. Bayesian estimation of the number of species from Poisson-Lindley stochastic abundance model using non-informative priors. Comput Stat (2024). https://doi.org/10.1007/s00180-024-01464-7

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