IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v60y2004i2p536-542.html
   My bibliography  Save this article

Sensitivity of Bayes Estimators to Hyper-Parameters with an Application to Maximum Yield from Fisheries

Author

Listed:
  • Russell B. Millar

Abstract

No abstract is available for this item.

Suggested Citation

  • Russell B. Millar, 2004. "Sensitivity of Bayes Estimators to Hyper-Parameters with an Application to Maximum Yield from Fisheries," Biometrics, The International Biometric Society, vol. 60(2), pages 536-542, June.
    • Handle: RePEc:bla:biomet:v:60:y:2004:i:2:p:536-542
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2004.00201.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. James Berger & Elías Moreno & Luis Pericchi & M. Bayarri & José Bernardo & Juan Cano & Julián Horra & Jacinto Martín & David Ríos-Insúa & Bruno Betrò & A. Dasgupta & Paul Gustafson & Larry Wasserman &, 1994. "An overview of robust Bayesian analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 3(1), pages 5-124, June.
    2. Russell B. Millar & Renate Meyer, 2000. "Non‐linear state space modelling of fisheries biomass dynamics by using Metropolis‐Hastings within‐Gibbs sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(3), pages 327-342.
    3. Sivaganesan, Siva, 1999. "A likelihood based robust Bayesian summary," Statistics & Probability Letters, Elsevier, vol. 43(1), pages 5-12, May.
    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. Müller, Ulrich K., 2012. "Measuring prior sensitivity and prior informativeness in large Bayesian models," Journal of Monetary Economics, Elsevier, vol. 59(6), pages 581-597.

    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. Hansen, Lars Peter, 2013. "Uncertainty Outside and Inside Economic Models," Nobel Prize in Economics documents 2013-7, Nobel Prize Committee.
    2. Ho, Paul, 2023. "Global robust Bayesian analysis in large models," Journal of Econometrics, Elsevier, vol. 235(2), pages 608-642.
    3. Chamberlain, Gary, 2000. "Econometrics and decision theory," Journal of Econometrics, Elsevier, vol. 95(2), pages 255-283, April.
    4. Chaloupka, Milani & Balazs, George, 2007. "Using Bayesian state-space modelling to assess the recovery and harvest potential of the Hawaiian green sea turtle stock," Ecological Modelling, Elsevier, vol. 205(1), pages 93-109.
    5. Zhichao Liu & Catherine Forbes & Heather Anderson, 2017. "Robust Bayesian exponentially tilted empirical likelihood method," Monash Econometrics and Business Statistics Working Papers 21/17, Monash University, Department of Econometrics and Business Statistics.
    6. Christoph Berninger & Almond Stocker & David Rugamer, 2020. "A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction," Papers 2006.05750, arXiv.org, revised Feb 2021.
    7. Dan J. Spitzner, 2023. "Calibrated Bayes factors under flexible priors," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 733-767, September.
    8. Sinha, Pankaj & Jayaraman, Prabha, 2009. "Robustness of Bayesian results for Inverse Gaussian distribution under ML-II epsilon-contaminated and Edgeworth Series class of prior distributions," MPRA Paper 15396, University Library of Munich, Germany.
    9. Min-Je Choi & Do-Hoon Kim, 2020. "Assessment and Management of Small Yellow Croaker ( Larimichthys polyactis ) Stocks in South Korea," Sustainability, MDPI, vol. 12(19), pages 1-17, October.
    10. Arielle Anderer & Hamsa Bastani & John Silberholz, 2022. "Adaptive Clinical Trial Designs with Surrogates: When Should We Bother?," Management Science, INFORMS, vol. 68(3), pages 1982-2002, March.
    11. Gómez Déniz, E. & Pérez Sánchez, J. M., 2001. "Fijación de primas de seguros bajo técnicas de robustez bayesiana," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 19, pages 5-20, Diciembre.
    12. Dragon Yongjun Tang, 2014. "Potential losses from incorporating return predictability into portfolio allocation," Australian Journal of Management, Australian School of Business, vol. 39(1), pages 35-45, February.
    13. Raffaella Giacomini & Toru Kitagawa & Matthew Read, 2021. "Robust Bayesian Analysis for Econometrics," Working Paper Series WP-2021-11, Federal Reserve Bank of Chicago.
    14. Ming Ouyang & Xinyuan Song, 2020. "Bayesian Local Influence of Generalized Failure Time Models with Latent Variables and Multivariate Censored Data," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 298-316, July.
    15. Gómez Déniz, E. & Hernández Bastida, A. & Vázquez Polo, F.J., 1998. "Un Análisis de Sensibilidad del Proceso de Tarificación en los Seguros Generales," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 9, pages 19-34, Junio.
    16. Sánchez-Sánchez, M. & Sordo, M.A. & Suárez-Llorens, A. & Gómez-Déniz, E., 2019. "Deriving Robust Bayesian Premiums Under Bands Of Prior Distributions With Applications," ASTIN Bulletin, Cambridge University Press, vol. 49(1), pages 147-168, January.
    17. Boratyńska Agata, 2021. "Robust Bayesian insurance premium in a collective risk model with distorted priors under the generalised Bregman loss," Statistics in Transition New Series, Polish Statistical Association, vol. 22(3), pages 123-140, September.
    18. Boratyńska, Agata, 2017. "Robust Bayesian estimation and prediction of reserves in exponential model with quadratic variance function," Insurance: Mathematics and Economics, Elsevier, vol. 76(C), pages 135-140.
    19. M. Perone-Pacifico & G. Salinetti & L. Tardella, 1996. "Bayesian robustness on constrained density band classes," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 5(2), pages 395-409, December.
    20. Gómez-Villegas, Miguel A. & Sanz, Luis, 2000. "[var epsilon]-contaminated priors in testing point null hypothesis: a procedure to determine the prior probability," Statistics & Probability Letters, Elsevier, vol. 47(1), pages 53-60, March.

    More about this item

    Statistics

    Access and download statistics

    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:bla:biomet:v:60:y:2004:i:2:p:536-542. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.