Testing multivariate normality in incomplete data of small sample size
Testing multivariate normality in incomplete data of small sample size
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在小样本量的不完整数据中检验多元正态性
DOI：
10.1016/j.jmva.2004.02.014
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发表时间：
2005
影响因子：
1.6
通讯作者：
Gang Wei
中科院分区：
文献类型：
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作者：
M. Tan;Hong;G. Tian;Gang Wei
在小样本和不完整数据的纵向研究中，基于多元正态分布的模型仍然是一个强大的分析工具。这包括广泛的生物医学研究。检验多变量正态性（MVN）假设至关重要。虽然有许多方法可以用于大样本完全数据的正态性检验，但很少涉及小样本的检验。例如，Liang等人（J. Statist. Planning and Inference 86（2000）129）提出了一种投影程序，用于在小样本情况下测试MVN的完整数据，其中样本大小可能接近维度。据我们所知，还没有统计方法来测试MVN在不完整的数据与小样本。本文开发了一个测试程序，在这样的设置使用多重插补和投影检验。为了利用多重填补中的不完全数据结构，我们采用了非迭代逆贝叶斯公式（IBF）抽样程序，而不是迭代吉布斯抽样生成iid样本。当样本量小于维数时，对完全数据和不完全数据进行模拟。该方法说明了一个真实的研究抗癌药物。
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.
DOI：
--
发表时间：
2000-10
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
P. Houghton;C. Stewart;P. Cheshire;L. Richmond;M. Kirstein;C. Poquette;M. Tan;H. Friedman;T. Brent
通讯作者：
P. Houghton;C. Stewart;P. Cheshire;L. Richmond;M. Kirstein;C. Poquette;M. Tan;H. Friedman;T. Brent