Testing Equality of Mean Vectors with Block-Circular and Block Compound-Symmetric Covariance Matrices

2021 ◽  
pp. 157-201
Author(s):  
Carlos A. Coelho
Keyword(s):  
Covariance Matrices ◽  
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.

Classification Based on Multivariate Repeated Measures with Time Effect on Mean Vector and an AR (1) Correlation Structure on the Repeated Measures

2005 ◽  
Vol 57 (1-2) ◽  
pp. 49-66 ◽  
Author(s):  
Anuradba Roy ◽  
Ravindra Khattree
Keyword(s):  
Maximum Likelihood ◽  
Repeated Measures ◽  
Likelihood Estimation ◽  
Covariance Matrices ◽  
Time Effect ◽  
Population Parameters ◽  
Time Effects ◽  
Mean Vectors ◽  
Mean Vector ◽  
Over Time

In repeated measures studies how observations change over time is often of prime interest. Modelling this time effect in the context of discrimination, is the objective of this article. We study the problem of classification with multiple q-variate observations with time effect on each individual. The covariance matrices as well as mean vectors are mordelled respectively to accommodate the correlation between the successive repeated measures and to describe the time effects. Computation schemes for maximum likelihood estimation of required population parameters are provided.

Distribution-free tests of mean vectors and covariance matrices for multivariate paired data

Metrika ◽  
2011 ◽  
Vol 75 (6) ◽  
pp. 833-854 ◽  
Author(s):  
Erning Li ◽  
Johan Lim ◽  
Kyunga Kim ◽  
Shin-Jae Lee
Keyword(s):  
Covariance Matrices ◽  
Paired Data ◽  
Distribution Free ◽  
Mean Vectors
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