• Login
    View Item 
    •   Maryland Shared Open Access Repository Home
    • ScholarWorks@UMBC
    • UMBC College of Natural and Mathematical Sciences
    • UMBC Mathematics and Statistics Department
    • View Item
    •   Maryland Shared Open Access Repository Home
    • ScholarWorks@UMBC
    • UMBC College of Natural and Mathematical Sciences
    • UMBC Mathematics and Statistics Department
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Rectangular Confidence Regions and Prediction Regions in Multivariate Calibration

    Files
    Calibration-R.pdf (373.2Kb)
    Links to Files
    https://link.springer.com/article/10.1007/s41096-022-00116-7
    Permanent Link
    https://doi.org/10.1007/s41096-022-00116-7
    http://hdl.handle.net/11603/24685
    Collections
    • UMBC Faculty Collection
    • UMBC Mathematics and Statistics Department
    Metadata
    Show full item record
    Author/Creator
    Lucagbo, Michael Daniel
    Mathew, Thomas
    Author/Creator ORCID
    https://orcid.org/0000-0003-4152-3628
    Date
    2022-03-28
    Type of Work
    18 pages
    Text
    journal articles
    postprints
    Citation of Original Publication
    Lucagbo, M.D., Mathew, T. Rectangular Confidence Regions and Prediction Regions in Multivariate Calibration. J Indian Soc Probab Stat (2022). https://doi.org/10.1007/s41096-022-00116-7
    Rights
    This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s41096-022-00116-7
    Access to this item will begin 03/28/2023
    Abstract
    The multivariate calibration problem deals with inference concerning an unknown value of a covariate vector based on an observation on a response vector. Two distinct scenarios are considered in the multivariate calibration problem: controlled calibration where the covariates are non-stochastic, and random calibration where the covariates are random. Under controlled calibration, a problem of interest is the computation of a confidence region for the unknown covariate vector. Under random calibration, the problem is that of computing a prediction region for the covariate vector. Assuming the standard multivariate normal linear regression model, rectangular confidence and prediction regions are derived using a parametric bootstrap approach. Numerical results show that the regions accurately maintain the coverage probabilities. The results are illustrated using examples. The regions currently available in the literature are all ellipsoidal, and this work is the first attempt to derive rectangular regions.


    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.

     

     

    My Account

    LoginRegister

    Browse

    This CollectionBy Issue DateTitlesAuthorsSubjectsType

    Statistics

    View Usage Statistics


    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.