IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v79y2009i9p1155-1161.html
   My bibliography  Save this article

Minimum Message Length shrinkage estimation

Author

Listed:
  • Makalic, Enes
  • Schmidt, Daniel F.

Abstract

This note considers estimation of the mean of a multivariate Gaussian distribution with known variance within the Minimum Message Length (MML) framework. Interestingly, the resulting MML estimator exactly coincides with the positive-part James-Stein estimator under the choice of an uninformative prior. A new approach for estimating parameters and hyperparameters in general hierarchical Bayes models is also presented.

Suggested Citation

  • Makalic, Enes & Schmidt, Daniel F., 2009. "Minimum Message Length shrinkage estimation," Statistics & Probability Letters, Elsevier, vol. 79(9), pages 1155-1161, May.
    • Handle: RePEc:eee:stapro:v:79:y:2009:i:9:p:1155-1161
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(09)00008-X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Hansen M. H & Yu B., 2001. "Model Selection and the Principle of Minimum Description Length," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 746-774, June.
    Full references (including those not matched with items on IDEAS)

    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. Seongkyoon Jeong & Jae Young Choi, 2012. "The taxonomy of research collaboration in science and technology: evidence from mechanical research through probabilistic clustering analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 719-735, June.
    2. Poskitt, D.S. & Sengarapillai, Arivalzahan, 2013. "Description length and dimensionality reduction in functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 98-113.
    3. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    4. Njindan Iyke, Bernard, 2015. "Macro Determinants of the Real Exchange Rate in a Small Open Small Island Economy: Evidence from Mauritius via BMA," MPRA Paper 68968, University Library of Munich, Germany.
    5. Paul L. Bowen & Robert A. O'Farrell & Fiona H. Rohde, 2009. "An Empirical Investigation of End-User Query Development: The Effects of Improved Model Expressiveness vs. Complexity," Information Systems Research, INFORMS, vol. 20(4), pages 565-584, December.
    6. Magkonis, Georgios & Zekente, Kalliopi-Maria, 2020. "Inflation-output trade-off: Old measures, new determinants?," Journal of Macroeconomics, Elsevier, vol. 65(C).
    7. Branimir Jovanovic, 2017. "Growth forecast errors and government investment and consumption multipliers," International Review of Applied Economics, Taylor & Francis Journals, vol. 31(1), pages 83-107, January.
    8. Klaus Wohlrabe & Teresa Buchen, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area and Germany," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 231-242, July.
    9. Jan G. De Gooijer & Ao Yuan, 2008. "MDL Mean Function Selection in Semiparametric Kernel Regression Models," Tinbergen Institute Discussion Papers 08-046/4, Tinbergen Institute.
    10. Sylvain Barde, 2017. "A Practical, Accurate, Information Criterion for Nth Order Markov Processes," Computational Economics, Springer;Society for Computational Economics, vol. 50(2), pages 281-324, August.
    11. Ivan Chang, Yuan-Chin & Huang, Yufen & Huang, Yu-Pai, 2010. "Early stopping in L2Boosting," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2203-2213, October.
    12. Branimir Jovanovic, 2012. "How Policy Actions Affect Short-term Post-crisis Recovery?," CEIS Research Paper 253, Tor Vergata University, CEIS, revised 05 Oct 2012.
    13. Leitenstorfer, Florian & Tutz, Gerhard, 2007. "Knot selection by boosting techniques," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4605-4621, May.
    14. Brian Hanlon & Catherine Forbes, 2002. "Model Selection Criteria for Segmented Time Series from a Bayesian Approach to Information Compression," Monash Econometrics and Business Statistics Working Papers 8/02, Monash University, Department of Econometrics and Business Statistics.
    15. Ching-Kang Ing, 2005. "Accumulated Prediction Errors, Information Criteria And Optimal Forecasting For Autoregressive Time Series," Econometrics 0503020, University Library of Munich, Germany.
    16. In-Koo Cho & Ken Kasa, 2012. "Model Validation and Learning," Discussion Papers dp12-07, Department of Economics, Simon Fraser University.
    17. repec:idn:journl:v:21:y:2018-12:i:1:p:1-24 is not listed on IDEAS
    18. Linda Mhalla & Valérie Chavez‐Demoulin & Debbie J. Dupuis, 2020. "Causal mechanism of extreme river discharges in the upper Danube basin network," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 741-764, August.
    19. Jie Ding & Vahid Tarokh & Yuhong Yang, 2018. "Model Selection Techniques -- An Overview," Papers 1810.09583, arXiv.org.
    20. David Kaplan, 2021. "On the Quantification of Model Uncertainty: A Bayesian Perspective," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 215-238, March.
    21. Fildes, Robert, 2006. "The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion," International Journal of Forecasting, Elsevier, vol. 22(3), pages 415-432.

    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:eee:stapro:v:79:y:2009:i:9:p:1155-1161. 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.