IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v59y2005i1p45-56.html
   My bibliography  Save this article

DIC in variable selection

Author

Listed:
  • Angelika van der Linde

Abstract

Model comparison is discussed from an information theoretic point of view. In particular the posterior predictive entropy is related to the target yielding DIC and modifications thereof. The adequacy of criteria for posterior predictive model comparison is also investigated depending on the comparison to be made. In particular variable selection as a special problem of model choice is formalized in different ways according to whether the comparison is a comparison across models or within an encompassing model and whether a joint or conditional sampling scheme is applied. DIC has been devised for comparisons across models. Its use in variable selection and that of other criteria is illustrated for a simulated data set.

Suggested Citation

  • Angelika van der Linde, 2005. "DIC in variable selection," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 59(1), pages 45-56, February.
    • Handle: RePEc:bla:stanee:v:59:y:2005:i:1:p:45-56
    DOI: 10.1111/j.1467-9574.2005.00278.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9574.2005.00278.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9574.2005.00278.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

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


    Cited by:

    1. Briana J. K. Stephenson & Amy H. Herring & Andrew F. Olshan, 2022. "Derivation of maternal dietary patterns accounting for regional heterogeneity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1957-1977, November.
    2. David Cutts & Edward Fieldhouse, 2009. "What Small Spatial Scales Are Relevant as Electoral Contexts for Individual Voters? The Importance of the Household on Turnout at the 2001 General Election," American Journal of Political Science, John Wiley & Sons, vol. 53(3), pages 726-739, July.
    3. Mostafa Sharafeldin & Omar Albatayneh & Ahmed Farid & Khaled Ksaibati, 2022. "A Bayesian Approach to Examine the Impact of Pavement Friction on Intersection Safety," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
    4. Shriner, Daniel & Yi, Nengjun, 2009. "Deviance information criterion (DIC) in Bayesian multiple QTL mapping," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1850-1860, March.
    5. Mohammed H. AbuJarad & Eman S. A. AbuJarad & Athar Ali Khan, 2022. "Bayesian Survival Analysis of Type I General Exponential Distributions," Annals of Data Science, Springer, vol. 9(2), pages 347-367, April.
    6. Papastamoulis, Panagiotis, 2018. "Overfitting Bayesian mixtures of factor analyzers with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 220-234.
    7. Rankin, Peter Sheldon & Lemos, Ricardo T., 2015. "An alternative surplus production model," Ecological Modelling, Elsevier, vol. 313(C), pages 109-126.
    8. Piou, Cyril & Berger, Uta & Grimm, Volker, 2009. "Proposing an information criterion for individual-based models developed in a pattern-oriented modelling framework," Ecological Modelling, Elsevier, vol. 220(17), pages 1957-1967.
    9. Follett, Lendie & Naald, Brian Vander, 2023. "Heterogeneity in choice experiment data: A Bayesian investigation," Journal of choice modelling, Elsevier, vol. 46(C).
    10. Amir Dezfouli & Kristi Griffiths & Fabio Ramos & Peter Dayan & Bernard W Balleine, 2019. "Models that learn how humans learn: The case of decision-making and its disorders," PLOS Computational Biology, Public Library of Science, vol. 15(6), pages 1-33, June.

    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:stanee:v:59:y:2005:i:1:p:45-56. 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.

    We have no bibliographic references for this item. You can help adding them by using 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=0039-0402 .

    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.