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:: Volume 17, Issue 1 (9-2023) ::
JSS 2023, 17(1): 0-0 Back to browse issues page
A Permutation Test for Multiple Correlation Coefficient in High Dimensional Normal Data
Dariush Najarzadeh *
Abstract:   (1520 Views)
In multiple regression analysis, the population multiple correlation coefficient (PMCC)  is widely used to    measure the correlation between a variable and a set of variables. To evaluate the existence or non-existence of this type of correlation, testing the hypothesis of zero  PMCC can be very useful. In high-dimensional data, due to the singularity of the sample covariance matrix, traditional testing procedures to test this hypothesis lose their applicability. A simple test statistic was proposed for zero  PMCC  based on a plug-in estimator of the sample covariance matrix inverse. Then, a permutation test was constructed based on the proposed test statistic to test the null hypothesis. A  simulation study was carried out to evaluate the performance of the proposed test in both high-dimensional and low-dimensional normal data sets. This study was finally ended by applying the proposed approach to mice tumour volumes data.
Keywords: Multiple correlation coefficient, High-dimensional normal data, Sparse precision matrix, Plug-in estimator, Permutation test
Full-Text [PDF 293 kb]   (1155 Downloads)    
Type of Study: Research | Subject: Statistical Inference
Received: 2023/02/24 | Accepted: 2023/09/1 | Published: 2023/07/11
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Najarzadeh D. A Permutation Test for Multiple Correlation Coefficient in High Dimensional Normal Data. JSS 2023; 17 (1)
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 17, Issue 1 (9-2023) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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