IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i7p1136-d785287.html
   My bibliography  Save this article

Asymptotic Efficiency of Point Estimators in Bayesian Predictive Inference

Author

Listed:
  • Emanuele Dolera

    (Department of Mathematics, University of Pavia, Via Adolfo Ferrata 5, 27100 Pavia, Italy
    Collegio Carlo Alberto, Piazza V. Arbarello 8, 10134 Torino, Italy)

Abstract

The point estimation problems that emerge in Bayesian predictive inference are concerned with random quantities which depend on both observable and non-observable variables. Intuition suggests splitting such problems into two phases, the former relying on estimation of the random parameter of the model, the latter concerning estimation of the original quantity from the distinguished element of the statistical model obtained by plug-in of the estimated parameter in the place of the random parameter. This paper discusses both phases within a decision theoretic framework. As a main result, a non-standard loss function on the space of parameters, given in terms of a Wasserstein distance, is proposed to carry out the first phase. Finally, the asymptotic efficiency of the entire procedure is discussed.

Suggested Citation

  • Emanuele Dolera, 2022. "Asymptotic Efficiency of Point Estimators in Bayesian Predictive Inference," Mathematics, MDPI, vol. 10(7), pages 1-27, April.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1136-:d:785287
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/7/1136/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/7/1136/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thyrion, P., 1960. "Contribution a l'etude du bonus pour non sinistre en assurance automobile," ASTIN Bulletin, Cambridge University Press, vol. 1(3), pages 142-162, April.
    2. Stefano Favaro & Antonio Lijoi & Igor Prünster, 2012. "A New Estimator of the Discovery Probability," Biometrics, The International Biometric Society, vol. 68(4), pages 1188-1196, December.
    3. 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.
    4. Rajiv Sambasivan & Sourish Das & Sujit K. Sahu, 2020. "A Bayesian perspective of statistical machine learning for big data," Computational Statistics, Springer, vol. 35(3), pages 893-930, September.
    5. Stefano Favaro & Antonio Lijoi & Igor Prünster, 2012. "A new estimator of the discovery probability," DEM Working Papers Series 007, University of Pavia, Department of Economics and Management.
    6. Stefano Favaro & Antonio Lijoi & Ramsés H. Mena & Igor Prünster, 2009. "Bayesian non‐parametric inference for species variety with a two‐parameter Poisson–Dirichlet process prior," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 993-1008, November.
    7. Dolera, Emanuele & Mainini, Edoardo, 2020. "On uniform continuity of posterior distributions," Statistics & Probability Letters, Elsevier, vol. 157(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Emanuele Dolera, 2022. "Preface to the Special Issue on “Bayesian Predictive Inference and Related Asymptotics—Festschrift for Eugenio Regazzini’s 75th Birthday”," Mathematics, MDPI, vol. 10(15), pages 1-4, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. repec:dau:papers:123456789/13437 is not listed on IDEAS
    5. 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.
    6. Antonio Canale & Igor Prünster, 2017. "Robustifying Bayesian nonparametric mixtures for count data," Biometrics, The International Biometric Society, vol. 73(1), pages 174-184, March.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Emanuele Dolera & Stefano Favaro, 2021. "A Compound Poisson Perspective of Ewens–Pitman Sampling Model," Mathematics, MDPI, vol. 9(21), pages 1-12, November.
    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. 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.
    14. Roberto Fontana, 2015. "Optimal design generation: an approach based on discovery probability," Computational Statistics, Springer, vol. 30(4), pages 1231-1244, December.
    15. Hirofumi Michimae & Takeshi Emura, 2022. "Bayesian ridge estimators based on copula-based joint prior distributions for regression coefficients," Computational Statistics, Springer, vol. 37(5), pages 2741-2769, November.
    16. Stefano Favaro & Antonio Lijoi & Igor Prünster, 2012. "A New Estimator of the Discovery Probability," Biometrics, The International Biometric Society, vol. 68(4), pages 1188-1196, December.
    17. Stefano Favaro & Antonio Lijoi & Igor Prünster, 2012. "A new estimator of the discovery probability," DEM Working Papers Series 007, University of Pavia, Department of Economics and Management.
    18. 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.
    19. 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.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1136-:d:785287. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.