Comparison and anti-concentration bounds for maxima of Gaussian random vectors

Victor Chernozhukov, Denis Chetverikov () and Kengo Kato
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Denis Chetverikov: Institute for Fiscal Studies and UCLA
Kengo Kato: Institute for Fiscal Studies

No CWP71/13, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: Slepian and Sudakov-Fernique type inequalities, which compare expectations of maxima of Gaussian random vectors under certain restrictions on the covariance matrices, play an important role in the probability theory, especially in empirical process and extreme value theories. Here we give explicit comparisons of expectations of smooth functions and distribution functions of maxima of Gaussian random vectors without any restriction on the covariance matrices. We also establish an anti-concentration inequality for maxima of Gaussian random vectors, which derives a useful upper bound on the Lévy concentration function for the maximum of (not necessarily independent) Gaussian random variables. The bound is universal and applies to vectors with arbitrary covariance matrices. This anti-concentration inequality plays a crucial role in establishing bounds on the Kolmogorov distance between maxima of Gaussian random vectors. These results have immediate applications in mathematical statistics. As an example of application, we establish a conditional multiplier central limit theorem for maxima of sums of independent random vectors where the dimension of the vectors is possibly much larger than the sample size.

Date: 2013-12-30
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Citations: View citations in EconPapers (10)

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Related works:
Working Paper: Comparison and anti-concentration bounds for maxima of Gaussian random vectors (2016)
Working Paper: Comparison and anti-concentration bounds for maxima of Gaussian random vectors (2016)
Working Paper: Comparison and anti-concentration bounds for maxima of Gaussian random vectors (2013)
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