Multiple Imputation Methods for Multivariate One‐Sided Tests with Missing Data
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.