Asymptotic Results of a High Dimensional MANOVA Test and Power Comparison When the Dimension is Large Compared to the Sample Size

By Yasunori Fujikoshi, Tetsuto Himeno, H. Wakaki. 2004

81 citations recorded by the source corpus.

This paper is concerned with Dempster trace criterion for multivariate linear hypothesis which was proposed for high dimensional situation. First we derive asymptotic null and nonnull distributions of Dempster trace criterion when both the sample size and the dimension tend to infinity. Our approximations are examined through some numerical experiments. Next we compare the power of Dempster trace criterion with the ones of three classical criteria; likelihood ratio criterion, Lawley-Hotelling trace criterion, and Bartlett-Nanda-Pillai trace criterion when the dimension is large compared to the sample size.

Subfields: Finance, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

1. Muni S. Srivastava
2. Chen Wang
3. Z. Bai

In Statistics and Probability

1. Z. Bai
2. Jiang Hu
3. Chen Wang
4. Wei Wang
5. Muni S. Srivastava
6. Jianfeng Yao
7. Tommaso Cai
8. Shu-rong Zheng

Citations in the corpus

• Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, neutral): “…by our inference framework may be applied to improve the interpretability of neuroimaging based SVM classifier it should not be confused for multivariate statistical testing frameworks like MANOVA Casella and Berger (2002) Srivastava and Du (2008) Fujikoshi et al. (2004) (or its equivalents).”
• A test for the mean vector with fewer observations than the dimension (2008, background): “[7] have generalized the T2 and T3 tests to MANOVA case when p/n → c, c ∈ (0, ∞).”
• Multivariate Theory for Analyzing High Dimensional Data (2007, background): “For some other tests and power , see Srivastava and Fujikoshi (2006), and when (p/n) → c, c ∈ (0,∞), see Fujikoshi et al. (2004).”
• On testing the equality of high dimensional mean vectors with unequal covariance matrices (2014, background): “Since then, more high dimensional tests have been proposed by Bai and Saranadasa (1996), Fujikoshi et al. (2004), Srivastava and Fujikoshi (2006), Srivastava (2007), Schott (2007), Srivastava and Du (2008), Srivastava (2009), Srivastava and Yanagihara (2010), Chen and Qin (2010) and Srivastava et…”