scholarly journals On testing the equality of mean vectors in high dimension

2013 ◽  
Vol 17 (1) ◽  
pp. 31 ◽  
Author(s):  
Muni S. Srivastava
Keyword(s):  
High Dimension ◽  
Mean Vectors ◽  
Equality Of Mean Vectors

Robustness of Yao’s, James’, and Johansen’s Tests Under Variance-Covariance Heteroscedasticity and Nonnormality

1991 ◽  
Vol 16 (2) ◽  
pp. 125-139 ◽  
Author(s):  
James Algina ◽  
Takako C. Oshima ◽  
K. Linda Tang
Keyword(s):  
Type I Error ◽  
Error Rates ◽  
Second Order ◽  
Type I ◽  
Lognormal Distributions ◽  
First Order ◽  
Type I Error Rates ◽  
Mean Vectors ◽  
Equality Of Mean Vectors

Type I error rates for Yao’s, James’ first order, James’ second order, and Johansen’s tests of equality of mean vectors for two independent samples were estimated for various conditions defined by the degree of heteroscedasticity and nonnormality (uniform, Laplace, t(5), beta (5, 1.5), exponential, and lognormal distributions). For these alternatives to Hotelling’s T2, variance-covariance homogeneity is not an assumption. Although the four procedures can be seriously nonrobust with exponential and lognormal distributions, they were fairly robust with the rest of the distributions. The performance of Yao’s test, James’ second order test, and Johansen’s test was slightly superior to the performance of James’ first order test.

Testing Equality of Mean Vectors in Two Sample Problem with Missing Data

2010 ◽  
Vol 39 (3) ◽  
pp. 487-500 ◽  
Author(s):  
Nobumichi Shutoh ◽  
Makiko Kusumi ◽  
Wataru Morinaga ◽  
Shunichi Yamada ◽  
Takashi Seo
Keyword(s):  
Missing Data ◽  
Sample Problem ◽  
Two Sample Problem ◽  
Mean Vectors ◽  
Equality Of Mean Vectors

Type I Error Rates for Yao’s and James Tests of Equality of Mean Vectors Under VarianceCovariance Heteroscedasticity

1988 ◽  
Vol 13 (3) ◽  
pp. 281-290 ◽  
Author(s):  
James Algina ◽  
Kezhen L. Tang
Keyword(s):  
Sample Size ◽  
Type I Error ◽  
Error Rates ◽  
Covariance Matrices ◽  
Type I ◽  
Type I Error Rates ◽  
Mean Vectors ◽  
Equality Of Mean Vectors

For Yao’s and James’ tests, Type I error rates were estimated for various combinations of the number of variables (p), samplesize ratio (n1: n2), sample-size-to-variables ratio, and degree of heteroscedasticity. These tests are alternatives to Hotelling’s T2 and are intended for use when the variance-covariance matrices are not equal in a study using two independent samples. The performance of Yao’s test was superior to that of James’. Yao’s test had appropriate Type I error rates when p ≥ 10, (n1 + n2)/p ≥ 10, and 1:2 ≤ n1:n2 ≤ 2:1. When (n1 + n2)/p = 20, Yao’s test was robust when n1: n2 was 5:1, 3:1, and 4:1 and p was 2, 6, and 10, respectively.

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