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Moderate Deviations via Cumulants

Author

Listed:
  • Hanna Döring

    (Technische Universität Berlin)

  • Peter Eichelsbacher

    (Ruhr-Universität Bochum)

Abstract

The purpose of the present paper is to establish moderate deviation principles for a rather general class of random variables fulfilling certain bounds of the cumulants. We apply a celebrated lemma of the theory of large deviations probabilities due to Rudzkis, Saulis, and Statulevičius. The examples of random objects we treat include dependency graphs, subgraph-counting statistics in Erdös–Rényi random graphs and U-statistics. Moreover, we prove moderate deviation principles for certain statistics appearing in random matrix theory, namely characteristic polynomials of random unitary matrices and the number of particles in a growing box of random determinantal point processes such as the number of eigenvalues in the GUE or the number of points in Airy, Bessel, and sine random point fields.

Suggested Citation

  • Hanna Döring & Peter Eichelsbacher, 2013. "Moderate Deviations via Cumulants," Journal of Theoretical Probability, Springer, vol. 26(2), pages 360-385, June.
    • Handle: RePEc:spr:jotpro:v:26:y:2013:i:2:d:10.1007_s10959-012-0437-0
    DOI: 10.1007/s10959-012-0437-0
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    References listed on IDEAS

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    1. Deli Li & Andrew Rosalsky, 2004. "Precise lim sup behavior of probabilities of large deviations for sums of i.i.d. random variables," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2004, pages 1-12, January.
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    Cited by:

    1. Hafouta, Yeor, 2023. "Convergence rates in the functional CLT for α-mixing triangular arrays," Stochastic Processes and their Applications, Elsevier, vol. 161(C), pages 242-290.
    2. Peter Eichelsbacher & Matthias Löwe, 2019. "Lindeberg’s Method for Moderate Deviations and Random Summation," Journal of Theoretical Probability, Springer, vol. 32(2), pages 872-897, June.
    3. Deng Zhang, 2019. "Gaussian Fluctuations and Moderate Deviations of Eigenvalues in Unitary Invariant Ensembles," Journal of Theoretical Probability, Springer, vol. 32(4), pages 1647-1687, December.
    4. Deng Zhang, 2017. "Tridiagonal Random Matrix: Gaussian Fluctuations and Deviations," Journal of Theoretical Probability, Springer, vol. 30(3), pages 1076-1103, September.
    5. Aurelija Kasparavičiūtė & Dovilė Deltuvienė, 2017. "Asymptotic Expansion for the Distribution Density Function of the Compound Poisson Process in Large Deviations," Journal of Theoretical Probability, Springer, vol. 30(4), pages 1655-1676, December.

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