Testing equality of mean vectors in a one-way MANOVA with monotone missing data

2017 ◽  
Vol 47 (22) ◽  
pp. 5534-5546 ◽  
Author(s):  
Ayaka Yagi ◽  
Takashi Seo ◽  
Muni S. Srivastava
Keyword(s):  
Missing Data ◽  
Mean Vectors ◽  
Equality Of Mean Vectors

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

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 Two Mean Vectors with Two-Step Monotone Missing Data

2011 ◽  
Vol 31 (1-2) ◽  
pp. 117-135 ◽  
Author(s):  
Noriko Seko ◽  
Tamae Kawasaki ◽  
Takashi Seo
Keyword(s):  
Missing Data ◽  
Mean Vectors

scholarly journals Modified Likelihood Ratio Test for Sub-mean Vectors with Two-step Monotone Missing Data in Two-sample Problem

2021 ◽  
Vol 50 (1) ◽  
pp. 88-104
Author(s):  
Tamae Kawasaki ◽  
Takashi Seo
Keyword(s):  
Missing Data ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Null Distribution ◽  
Linear Interpolation ◽  
Ratio Test ◽  
Data Set ◽  
Chi Squared ◽  
Mean Vectors ◽  
Modified Likelihood

This article deals with the problem of testing for two normal sub-mean vectors when the data set have two-step monotone missing observations. Under the assumptions that the population covariance matrices are equal, we obtain the likelihood ratio test (LRT) statistic. Furthermore, an asymptotic expansion for the null distribution of the LRT statistic is derived under the two-step monotone missing data by the perturbation method. Using the result, we propose two improved statistics with good chi-squared approximation. One is the modified LRT statistic by Bartlett correction,and the other is the modified LRT statistic using the modification coefficient by linear interpolation. The accuracy of the approximations are investigated by using a Monte Carlo simulation. The proposed methods are illustrated using an example.

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