Testing multivariate normality in incomplete data of small sample size

Ming Tan, Hong-Bin Fang, Guo-Liang Tian and Gang Wei

Journal of Multivariate Analysis, 2005, vol. 93, issue 1, 164-179

Abstract: In longitudinal studies with small samples and incomplete data, multivariate normal-based models continue to be a powerful tool for analysis. This has included a broad scope of biomedical studies. Testing the assumption of multivariate normality (MVN) is critical. Although many methods are available for testing normality in complete data with large samples, a few deal with the testing in small samples. For example, Liang et al. (J. Statist. Planning and Inference 86 (2000) 129) propose a projection procedure for testing MVN for complete-data with small samples where the sample sizes may be close to the dimension. To our knowledge, no statistical methods for testing MVN in incomplete data with small samples are yet available. This article develops a test procedure in such a setting using multiple imputations and the projection test. To utilize the incomplete data structure in multiple imputation, we adopt a noniterative inverse Bayes formulae (IBF) sampling procedure instead of the iterative Gibbs sampling to generate iid samples. Simulations are performed for both complete and incomplete data when the sample size is less than the dimension. The method is illustrated with a real study on an anticancer drug.

Keywords: Bayesian; analysis; EM; algorithm; IBF; sampling; Multiple; imputation; Multivariate; normality; test; Projection; test (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (5)

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