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:: Volume 14, Issue 1 (8-2020) ::
JSS 2020, 14(1): 233-250 Back to browse issues page
A Test for Independence in High-Dimensional Normal Data
Dariush Najarzadeh *
Abstract:   (2893 Views)
The hypothesis of complete independence is necessary for many statistical inferences. Classical testing procedures can not be applied to test this hypothesis in high-dimensional data. In this paper, a simple test statistic is presented for testing complete independence in multivariate high dimensional normal data. Using the theory of martingales, the asymptotic normality of the test statistic is established. In order to evaluate the performance of the proposed test and compare it with existing procedures, a simulation study was conducted. The simulation results indicate that the proposed test has an empirical type-I error rate with an average relative error less than the available tests. An application of the proposed method for gene expression clinical prostate data is presented.
Keywords: Complete Independence Test, Multivariate Normal Distribution, High Dimensional Data, Martingale Theory.
Full-Text [PDF 209 kb]   (1047 Downloads)    
Type of Study: Applied | Subject: Statistical Inference
Received: 2019/01/11 | Accepted: 2019/11/8 | Published: 2020/02/20
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Najarzadeh D. A Test for Independence in High-Dimensional Normal Data. JSS 2020; 14 (1) :233-250
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 14, Issue 1 (8-2020) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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