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Optimal sample size for estimating the mean concentration of invasive organisms in ballast water via a semiparametric Bayesian analysis

Author

Listed:
  • Eliardo G. Costa

    (Universidade Federal do Rio Grande do Norte)

  • Carlos Daniel Paulino

    (Universidade de Lisboa)

  • Julio M. Singer

    (Universidade de São Paulo)

Abstract

We consider the determination of optimal sample sizes to estimate the concentration of organisms in ballast water via a semiparametric Bayesian approach involving a Dirichlet process mixture based on a Poisson model. This semiparametric model provides greater flexibility to model the organism distribution than that allowed by competing parametric models and is robust against misspecification. To obtain the optimal sample size we use a total cost minimization criterion, based on the sum of a Bayes risk and a sampling cost function. Credible intervals obtained via the proposed model may be used to verify compliance of the water with international standards before deballasting.

Suggested Citation

  • Eliardo G. Costa & Carlos Daniel Paulino & Julio M. Singer, 2023. "Optimal sample size for estimating the mean concentration of invasive organisms in ballast water via a semiparametric Bayesian analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 57-74, March.
    • Handle: RePEc:spr:stmapp:v:32:y:2023:i:1:d:10.1007_s10260-022-00639-0
    DOI: 10.1007/s10260-022-00639-0
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    References listed on IDEAS

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    1. Nils Hjort & Andrea Ongaro, 2005. "Exact Inference for Random Dirichlet Means," Statistical Inference for Stochastic Processes, Springer, vol. 8(3), pages 227-254, December.
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