Testing the Sphericity of a covariance matrix when the dimension is much larger than the sample size

Zeng Li; Jianfeng Yao

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arXiv · 1508.02498

Testing the Sphericity of a covariance matrix when the dimension is much larger than the sample size

Zeng Li·Jianfeng Yao

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Abstract

This paper focuses on the prominent sphericity test when the dimension $p$ is much lager than sample size $n$. The classical likelihood ratio test(LRT) is no longer applicable when $p\gg n$. Therefore a Quasi-LRT is proposed and asymptotic distribution of the test statistic under the null when $p/n\rightarrow\infty, n\rightarrow\infty$ is well established in this paper. Meanwhile, John's test has been found to possess the powerful {\it dimension-proof} property, which keeps exactly the same limiting distribution under the null with any $(n,p)$-asymptotic, i.e. $p/n\rightarrow[0,\infty]$, $n\rightarrow\infty$. All asymptotic results are derived for general population with finite fourth order moment. Numerical experiments are implemented for comparison.

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Zeng Li, Jianfeng Yao. 2015-08-11. Testing the Sphericity of a covariance matrix when the dimension is much larger than the sample size. https://arxiv.org/abs/1508.02498

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