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Multiple Imputation Methods for Multivariate One-Sided Tests with Missing Data

Authors

  • Tao Wang,

    Corresponding author
    1. Department of Statistics, University of British Columbia, Vancouver, BC, V6T 1Z2, Canada
    2. Center on Aging and Health, Johns Hopkins University, Baltimore, Maryland 21205, U.S.A.
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  • Lang Wu

    Corresponding author
    1. Department of Statistics, University of British Columbia, Vancouver, BC, V6T 1Z2, Canada
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email:twang46@jhmi.edu

email:lang@stat.ubc.ca

Abstract

Summary Multivariate one-sided hypotheses testing problems arise frequently in practice. Various tests have been developed. In practice, there are often missing values in multivariate data. In this case, standard testing procedures based on complete data may not be applicable or may perform poorly if the missing data are discarded. In this article, we propose several multiple imputation methods for multivariate one-sided testing problem with missing data. Some theoretical results are presented. The proposed methods are evaluated using simulations. A real data example is presented to illustrate the methods.

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