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Bayesian nonparametric inference for species variety with a two parameter Poisson-Dirichlet process prior

Author

Listed:
  • Stefano Favaro
  • Antonio Lijoi
  • Ramsés H. Mena
  • Igor Prünster

Abstract

A Bayesian nonparametric methodology has been recently proposed in order to deal with the issue of prediction within species sampling problems. Such problems concern the evaluation, conditional on a sample of size n, of the species variety featured by an additional sample of size m. Genomic applications pose the additional challenge of having to deal with large values of both n and m. In such a case the computation of the Bayesian nonparametric estimators is cumbersome and prevents their implementation. In this paper we focus on the two parameter Poisson-Dirichlet model and provide completely explicit expressions for the corresponding estimators, which can be easily evaluated for any sizes of n and m. We also study the asymptotic behaviour of the number of new species conditionally on the observed sample: such an asymptotic result allows, combined with a suitable simulation scheme, to derive asymptotic highest posterior density intervals for the estimates of interest. Finally, we illustrate the implementation of the proposed methodology by the analysis of five Expressed Sequence Tags (EST) datasets.

Suggested Citation

  • Stefano Favaro & Antonio Lijoi & Ramsés H. Mena & Igor Prünster, 2009. "Bayesian nonparametric inference for species variety with a two parameter Poisson-Dirichlet process prior," Carlo Alberto Notebooks 123, Collegio Carlo Alberto.
    • Handle: RePEc:cca:wpaper:123
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    Citations

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    Cited by:

    1. Emanuele Dolera & Stefano Favaro, 2021. "A Compound Poisson Perspective of Ewens–Pitman Sampling Model," Mathematics, MDPI, vol. 9(21), pages 1-12, November.
    2. Zhang, Junyi & Dassios, Angelos, 2023. "Truncated two-parameter Poisson-Dirichlet approximation for Pitman-Yor process hierarchical models," LSE Research Online Documents on Economics 120294, London School of Economics and Political Science, LSE Library.
    3. Emanuele Dolera, 2022. "Asymptotic Efficiency of Point Estimators in Bayesian Predictive Inference," Mathematics, MDPI, vol. 10(7), pages 1-27, April.
    4. Cesari, Oriana & Favaro, Stefano & Nipoti, Bernardo, 2014. "Posterior analysis of rare variants in Gibbs-type species sampling models," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 79-98.
    5. Stefano Favaro & Shui Feng & Fuqing Gao, 2018. "Moderate Deviations for Ewens-Pitman Sampling Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(2), pages 330-341, August.
    6. Julyan Arbel & Stefano Favaro, 2021. "Approximating Predictive Probabilities of Gibbs-Type Priors," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 496-519, February.
    7. repec:dau:papers:123456789/13437 is not listed on IDEAS
    8. Lawless Caroline & Arbel Julyan, 2019. "A simple proof of Pitman–Yor’s Chinese restaurant process from its stick-breaking representation," Dependence Modeling, De Gruyter, vol. 7(1), pages 45-52, March.
    9. Weixuan Zhu & Fabrizio Leisen, 2015. "A multivariate extension of a vector of two-parameter Poisson-Dirichlet processes," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(1), pages 89-105, March.
    10. Sonia Petrone & Stefano Rizzelli & Judith Rousseau & Catia Scricciolo, 2014. "Empirical Bayes methods in classical and Bayesian inference," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 201-215, August.
    11. Stefano Favaro & Bernardo Nipoti, 2014. "Discussion of “On simulation and properties of the stable law” by L. Devroye and L. James," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 365-369, August.
    12. Pierpaolo De Blasi & Stefano Favaro & Antonio Lijoi & Ramsés H. Mena & Igor Prünster & Mattteo Ruggiero, 2013. "Are Gibbs-type priors the most natural generalization of the Dirichlet process?," DEM Working Papers Series 054, University of Pavia, Department of Economics and Management.
    13. Giulia Cereda, 2017. "Bayesian approach to LR assessment in case of rare type match," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 71(2), pages 141-164, May.
    14. Giulia Cereda & Fabio Corradi & Cecilia Viscardi, 2023. "Learning the two parameters of the Poisson–Dirichlet distribution with a forensic application," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(1), pages 120-141, March.
    15. Giulia Cereda, 2017. "Impact of Model Choice on LR Assessment in Case of Rare Haplotype Match (Frequentist Approach)," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(1), pages 230-248, March.
    16. Stefano Favaro & Antonio Lijoi & Igor Prunster, 2011. "Asymptotics for a Bayesian nonparametric estimator of species richness," Quaderni di Dipartimento 144, University of Pavia, Department of Economics and Quantitative Methods.

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