  In this paper, we focus on testing multivariate normality using the BHEP test
with data that are missing completely at random. Our objective is twofold:
first, to gain insight into the asymptotic behavior of BHEP test statistics
under two widely used approaches for handling missing data, namely
complete-case analysis and imputation, and second, to compare the power
performance of test statistic under these approaches. It is observed that under
the imputation approach, the affine invariance of test statistics is not
preserved. To address this issue, we propose an appropriate bootstrap algorithm
for approximating p-values. Extensive simulation studies demonstrate that both
mean and median approaches exhibit greater power compared to testing with
complete-case analysis, and open some questions for further research.
