Volume 5, Issue 2 (Vol. 5,No. 2 2019)
mmr 2019, 5(2): 187-196 |
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Comparing Mean Vectors Via Generalized Inference in Multivariate Log-Normal Distributions ns. mmr 2019; 5 (2) :187-196
URL: http://mmr.khu.ac.ir/article-1-2711-en.html
URL: http://mmr.khu.ac.ir/article-1-2711-en.html
Abstract: (2194 Views)
Abstract
In this paper, we consider the problem of means in several multivariate log-normal distributions and propose a useful method called as generalized variable method. Simulation studies show that suggested method has a appropriate size and power regardless sample size. To evaluation this method, we compare this method with traditional MANOVA such that the actual sizes of the two methods are close but the power of test and coverage probability of proposed methods are better than MANOVA in most cases specially when the sample sizes are small. Therefore, we can use this method when the variance-covariance matrices are not equal and there is not a suitable method../files/site1/files/52/7.pdf
In this paper, we consider the problem of means in several multivariate log-normal distributions and propose a useful method called as generalized variable method. Simulation studies show that suggested method has a appropriate size and power regardless sample size. To evaluation this method, we compare this method with traditional MANOVA such that the actual sizes of the two methods are close but the power of test and coverage probability of proposed methods are better than MANOVA in most cases specially when the sample sizes are small. Therefore, we can use this method when the variance-covariance matrices are not equal and there is not a suitable method../files/site1/files/52/7.pdf
Keywords: Generalized Variable, Multivariate log-normal distribution, Size and power of test, Coverage probability, Cholesky decomposition.
Type of Study: Original Manuscript |
Subject:
stat
Received: 2017/11/26 | Revised: 2019/12/16 | Accepted: 2018/05/28 | Published: 2019/11/18 | ePublished: 2019/11/18
Received: 2017/11/26 | Revised: 2019/12/16 | Accepted: 2018/05/28 | Published: 2019/11/18 | ePublished: 2019/11/18
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