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An optimal projection test for zero multiple correlation coefficient in high-dimensional normal data
Communications in Statistics - Theory and Methods ( IF 0.8 ) Pub Date : 2020-04-24 , DOI: 10.1080/03610926.2020.1757111
D. Najarzadeh 1
Affiliation  

Abstract

Testing the hypothesis of zero multiple correlation coefficient is of interest in wide variety of applications including multiple regression analysis. In high-dimensional data, traditional testing procedures to test this hypothesis become practically infeasible due to the singularity of the sample covariance matrix. To deal with this problem, an optimal projection test with a computationally simple and efficient algorithm for implementation is proposed, which can also be used in low-dimensional data. Some simulations are performed to evaluate the performance of the proposed test in high-dimensional normal data as well as to compare the proposed test with the classical exact test in low-dimensional normal data. Lastly, the experimental validation of the proposed approach is carried out on mice tumor volumes data.



中文翻译:

高维正态数据中零多重相关系数的最优投影检验

摘要

检验零多重相关系数的假设在包括多元回归分析在内的各种应用中都很有趣。在高维数据中,由于样本协方差矩阵的奇异性,检验该假设的传统检验程序实际上变得不可行。针对这一问题,提出了一种计算简单、高效的最优投影测试算法来实现,该算法也可用于低维数据。进行了一些模拟以评估所提出的测试在高维正态数据中的性能,并将所提出的测试与低维正态数据中的经典精确测试进行比较。最后,对小鼠肿瘤体积数据进行了所提出方法的实验验证。

更新日期:2020-04-24
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