Chernozhukov, Victor Chetverikov, Denis Kengo Kato
Year of Publication:
cemmap working paper, Centre for Microdata Methods and Practice CWP71/13
Slepian and Sudakov-Fernique type inequalities, which com- pare expectations of maxima of Gaussian random vectors under certain restrictions on the covariance matrices, play an important role in proba- bility 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 with- out 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 inde- pendent random vectors where the dimension of the vectors is possibly much larger than the sample size.