IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v36y1997i2p205-212.html
   My bibliography  Save this article

A simple diagnostic tool for local prior sensitivity

Author

Listed:
  • Peña, Daniel
  • Zamar, Ruben

Abstract

This paper presents a simple diagnostic tool to assess the sensitivity of the posterior mode in the presence of an infinitesimal contamination in the prior distribution. The proposed diagnostic measure is easy to compute and can be used as a first step in judging the robustness of the Bayesian inference. The procedure is illustrated in the estimation of the mean of a normal distribution. Some extensions of this diagnostic measure to the multivariate case and credibility intervals are briefly discussed.

Suggested Citation

  • Peña, Daniel & Zamar, Ruben, 1997. "A simple diagnostic tool for local prior sensitivity," Statistics & Probability Letters, Elsevier, vol. 36(2), pages 205-212, December.
    • Handle: RePEc:eee:stapro:v:36:y:1997:i:2:p:205-212
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(97)00067-9
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

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

    Other versions of this item:

    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.
    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. Passarin Katia, 2004. "Local robustness measures for posterior summaries," Economics and Quantitative Methods qf0405, Department of Economics, University of Insubria.

    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. Chamberlain, Gary, 2000. "Econometrics and decision theory," Journal of Econometrics, Elsevier, vol. 95(2), pages 255-283, April.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Didier Dubois, 2010. "Representation, Propagation, and Decision Issues in Risk Analysis Under Incomplete Probabilistic Information," Risk Analysis, John Wiley & Sons, vol. 30(3), pages 361-368, March.
    7. Igor Kopylov, 2016. "Subjective probability, confidence, and Bayesian updating," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 62(4), pages 635-658, October.
    8. Pankaj Sinha & Ashok Bansal, 2008. "Bayesian optimization analysis with ML-II ε-contaminated prior," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(2), pages 203-211.
    9. Lars P. Hansen & Thomas J. Sargent, 2016. "Sets of Models and Prices of Uncertainty," NBER Working Papers 22000, National Bureau of Economic Research, Inc.
    10. Hansen, Lars Peter & Sargent, Thomas J., 2021. "Macroeconomic uncertainty prices when beliefs are tenuous," Journal of Econometrics, Elsevier, vol. 223(1), pages 222-250.
    11. Chamberlain Mbah & Kris Peremans & Stefan Van Aelst & Dries F. Benoit, 2019. "Robust Bayesian seemingly unrelated regression model," Computational Statistics, Springer, vol. 34(3), pages 1135-1157, September.
    12. Jacinto Martín & David Insua & Fabrizio Ruggeri, 2003. "Joint sensitivity in bayesian decision theory," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 173-194, June.
    13. Mohammad Jafari Jozani & Éric Marchand & Ahmad Parsian, 2012. "Bayesian and Robust Bayesian analysis under a general class of balanced loss functions," Statistical Papers, Springer, vol. 53(1), pages 51-60, February.
    14. Gómez Déniz, E. & Pérez Sánchez, J.M., 2001. "Buenos y malos riesgos en seguros: el punto de vista bayesiano basado en distribuciones bimodales," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 18, pages 175-187, Agosto.
    15. Jingjing He & Wei Wang & Min Huang & Shaohua Wang & Xuefei Guan, 2021. "Bayesian Inference under Small Sample Sizes Using General Noninformative Priors," Mathematics, MDPI, vol. 9(21), pages 1-20, November.
    16. R. Winkler & Javier Muñoz & José Cervera & José Bernardo & Gail Blattenberger & Joseph Kadane & Dennis Lindley & Allan Murphy & Robert Oliver & David Ríos-Insua, 1996. "Scoring rules and the evaluation of probabilities," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 5(1), pages 1-60, June.
    17. Agata Boratyńska, 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. Hansen, Lars Peter, 2013. "Uncertainty Outside and Inside Economic Models," Nobel Prize in Economics documents 2013-7, Nobel Prize Committee.
    19. Congdon, Peter, 2008. "A bivariate frailty model for events with a permanent survivor fraction and non-monotonic hazards; with an application to age at first maternity," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4346-4356, May.
    20. Giacomini, Raffaella & Kitagawa, Toru & Read, Matthew, 2021. "Robust Bayesian Analysis for Econometrics," CEPR Discussion Papers 16488, C.E.P.R. Discussion Papers.

    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:eee:stapro:v:36:y:1997:i:2:p:205-212. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.