• Login
    View Item 
    •   T-Stór
    • Other
    • Teagasc publications in Biomed Central
    • View Item
    •   T-Stór
    • Other
    • Teagasc publications in Biomed Central
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of T-StórCommunitiesPublication DateAuthorsTitlesSubjectsFunderThis CollectionPublication DateAuthorsTitlesSubjectsFunderProfilesView

    My Account

    LoginRegister

    Information

    Deposit AgreementLicense

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Estimation of prediction error variances via Monte Carlo sampling methods using different formulations of the prediction error variance

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    1297-9686-41-23.pdf
    Size:
    395.9Kb
    Format:
    PDF
    Download
    Author
    Hickey, John M
    Veerkamp, Roel F.
    Calus, M. P. L.
    Mulder, Han A
    Thompson, Robin
    Keyword
    Prediction error variance covariance matrix
    Monte Carlo sampling
    Convergence rate
    Breeding values
    Date
    2009-02-09
    
    Metadata
    Show full item record
    Statistics
    Display Item Statistics
    URI
    http://hdl.handle.net/11019/226; http://dx.doi.org/10.1186/1297-9686-41-23
    Citation
    John M Hickey, Roel F Veerkamp, Mario PL Calus, Han A Mulder and Robin Thompson. Estimation of prediction error variances via Monte Carlo sampling methods using different formulations of the prediction error variance. Genetics Selection Evolution. 2009 Feb 09;41(1):23.doi:10.1186/1297-9686-41-23
    Abstract
    Calculation of the exact prediction error variance covariance matrix is often computationally too demanding, which limits its application in REML algorithms, the calculation of accuracies of estimated breeding values and the control of variance of response to selection. Alternatively Monte Carlo sampling can be used to calculate approximations of the prediction error variance, which converge to the true values if enough samples are used. However, in practical situations the number of samples, which are computationally feasible, is limited. The objective of this study was to compare the convergence rate of different formulations of the prediction error variance calculated using Monte Carlo sampling. Four of these formulations were published, four were corresponding alternative versions, and two were derived as part of this study. The different formulations had different convergence rates and these were shown to depend on the number of samples and on the level of prediction error variance. Four formulations were competitive and these made use of information on either the variance of the estimated breeding value and on the variance of the true breeding value minus the estimated breeding value or on the covariance between the true and estimated breeding values.
    Collections
    Teagasc publications in Biomed Central

    entitlement

     
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.