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Moderate deviation principles for classical likelihood ratio tests of high-dimensional normal distributions

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  • Jiang, Hui
  • Wang, Shaochen

Abstract

Let x1,…,xn be a random sample from a Gaussian random vector of dimension p

Suggested Citation

  • Jiang, Hui & Wang, Shaochen, 2017. "Moderate deviation principles for classical likelihood ratio tests of high-dimensional normal distributions," Journal of Multivariate Analysis, Elsevier, vol. 156(C), pages 57-69.
    • Handle: RePEc:eee:jmvana:v:156:y:2017:i:c:p:57-69
    DOI: 10.1016/j.jmva.2017.02.004
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    References listed on IDEAS

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    1. Jurecková, J. & Kallenberg, W. C. M. & Veraverbeke, N., 1988. "Moderate and Cramer-type large deviation theorems for M-estimators," Statistics & Probability Letters, Elsevier, vol. 6(3), pages 191-199, February.
    2. Otsu, Taisuke, 2011. "Moderate deviations of generalized method of moments and empirical likelihood estimators," Journal of Multivariate Analysis, Elsevier, vol. 102(8), pages 1203-1216, September.
    3. Chen, Song Xi & Zhang, Li-Xin & Zhong, Ping-Shou, 2010. "Tests for High-Dimensional Covariance Matrices," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 810-819.
    4. Taisuke Otsu, 2011. "Large deviations of generalized method of moments and empirical likelihood estimators," Econometrics Journal, Royal Economic Society, vol. 14(2), pages 321-329, July.
    5. Jiang, Dandan, 2016. "Tests for large-dimensional covariance structure based on Rao’s score test," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 28-39.
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    Cited by:

    1. Bai, Yansong & Zhang, Yong & Liu, Congmin, 2023. "Moderate deviation principle for likelihood ratio test in multivariate linear regression model," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
    2. Dörnemann, Nina, 2023. "Likelihood ratio tests under model misspecification in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 193(C).

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