Simultaneous Test for Independence Among Subvectors of Several Moderately High Dimensional Multivariate Normal Distributions
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Dariush Najarzadeh * |
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Abstract: (5983 Views) |
Testing the Hypothesis of independence of a p-variate vector subvectors, as a pretest for many others related tests, is always as a matter of interest. When the sample size n is much larger than the dimension p, the likelihood ratio test (LRT) with chisquare approximation, has an acceptable performance. However, for moderately high-dimensional data by which n is not much larger than p, the chisquare approximation for null distribution of the LRT statistic is no more usable. As a general case, here, a simultaneous subvectors independence testing procedure in all k p-variate normal distributions is considered. To test this hypothesis, a normal approximation for the null distribution of the LRT statistic was proposed. A simulation study was performed to show that the proposed normal approximation outperforms the chisquare approximation. Finally, the proposed testing procedure was applied on prostate cancer data. |
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Keywords: Multivariate Normal Distribution, Likelihood Ratio Test, High-Dimensional Data, Testing Independence, Multivariate Gamma Function. |
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Full-Text [PDF 289 kb]
(1503 Downloads)
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Type of Study: Applied |
Subject:
Statistical Inference Received: 2018/01/30 | Accepted: 2018/09/5 | Published: 2019/02/25
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