IDEAS home Printed from https://ideas.repec.org/p/isu/genres/694.html
   My bibliography  Save this paper

Classical and Bayesian Prediction As Applied to an Unbalanced Mixed Linear Model

Author

Listed:
  • Harville, David A.
  • Carriquiry, Alicia L.

Abstract

No abstract is available for this item.

Suggested Citation

  • Harville, David A. & Carriquiry, Alicia L., 1992. "Classical and Bayesian Prediction As Applied to an Unbalanced Mixed Linear Model," Staff General Research Papers Archive 694, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:694
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. MacNab, Ying C. & Lin, Yi, 2009. "On empirical Bayes penalized quasi-likelihood inference in GLMMs and in Bayesian disease mapping and ecological modeling," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2950-2967, June.
    2. L. Kurkalova & A. Carriquiry, 2003. "Input- and Output-Oriented Technical Efficiency of Ukrainian Collective Farms, 1989–1992: Bayesian Analysis of a Stochastic Production Frontier Model," Journal of Productivity Analysis, Springer, vol. 20(2), pages 191-211, September.
    3. Marinela Capanu & Colin B. Begg, 2011. "Hierarchical Modeling for Estimating Relative Risks of Rare Genetic Variants: Properties of the Pseudo-Likelihood Method," Biometrics, The International Biometric Society, vol. 67(2), pages 371-380, 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:isu:genres:694. 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: Curtis Balmer (email available below). General contact details of provider: https://edirc.repec.org/data/deiasus.html .

    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.