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On the Tracy–Widom approximation of studentized extreme eigenvalues of Wishart matrices

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  • Deo, Rohit S.

Abstract

The few largest eigenvalues of Wishart matrices are useful in testing numerous hypotheses and are typically studentized as the noise variance is unknown. Specifically, the largest eigenvalue is studentized using the average trace of the matrix. However, this ratio has a distribution poorly approximated by its asymptotic one when either the sample size or dimension is not too large, making inference problematic. We present a simple variance adjustment that significantly improves the approximation and theoretically demonstrate the increase in power that this adjustment delivers compared to the power of the uncorrected studentized eigenvalue. We propose a bias corrected consistent estimator of the noise variance when studentizing the (k+1)st largest eigenvalue in the presence of exactly k spikes and a variance correction for the resulting studentized eigenvalue is proposed.

Suggested Citation

  • Deo, Rohit S., 2016. "On the Tracy–Widom approximation of studentized extreme eigenvalues of Wishart matrices," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 265-272.
    • Handle: RePEc:eee:jmvana:v:147:y:2016:i:c:p:265-272
    DOI: 10.1016/j.jmva.2016.01.010
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    References listed on IDEAS

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    1. Nadler, Boaz, 2011. "On the distribution of the ratio of the largest eigenvalue to the trace of a Wishart matrix," Journal of Multivariate Analysis, Elsevier, vol. 102(2), pages 363-371, February.
    2. Baik, Jinho & Silverstein, Jack W., 2006. "Eigenvalues of large sample covariance matrices of spiked population models," Journal of Multivariate Analysis, Elsevier, vol. 97(6), pages 1382-1408, July.
    3. Alexei Onatski, 2009. "Testing Hypotheses About the Number of Factors in Large Factor Models," Econometrica, Econometric Society, vol. 77(5), pages 1447-1479, September.
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