A two-sample test based on cluster subspaces for equality of mean vectors in high dimension

2017 ◽  
Vol 37 (1-2) ◽  
pp. 147 ◽  
Author(s):  
Łukasz Smaga
Keyword(s):  
High Dimension ◽  
Sample Test ◽  
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.

Robust two-sample test of high-dimensional mean vectors under dependence

2019 ◽  
Vol 169 ◽  
pp. 312-329
Author(s):  
Wei Wang ◽  
Nan Lin ◽  
Xiang Tang
Keyword(s):  
High Dimensional ◽  
Sample Test ◽  
Mean Vectors
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