Abstract
We establish explicit bounds on the convex distance between the distribution of a vector of smooth functionals of a Gaussian field and that of a normal vector with a positive-definite covariance matrix. Our bounds are commensurate to the ones obtained by Nourdin et al. (Ann Inst Henri Poincaré Probab Stat 46(1):45–58, 2010) for the (smoother) 1-Wasserstein distance, and do not involve any additional logarithmic factor. One of the main tools exploited in our work is a recursive estimate on the convex distance recently obtained by Schulte and Yukich (Electron J Probab 24(130):1–42, 2019). We illustrate our abstract results in two different situations: (i) we prove a quantitative multivariate fourth moment theorem for vectors of multiple Wiener–Itô integrals, and (ii) we characterize the rate of convergence for the finite-dimensional distributions in the functional Breuer–Major theorem.
Original language | English |
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Pages (from-to) | 2020-2037 |
Number of pages | 18 |
Journal | Journal of Theoretical Probability |
Volume | 35 |
Early online date | 4 Jun 2021 |
DOIs | |
Publication status | Published - 30 Sept 2022 |
Bibliographical note
Funding Information:We thank Simon Campese and Nicola Turchi for pointing out an error in an earlier version. I. Nourdin was supported by the FNR grant APOGee (R-AGR-3585-10) at Luxembourg University; G. Peccati is supported by the FNR grant FoRGES (R-AGR-3376-10) at Luxembourg University; X. Yang was supported by the FNR Grant MISSILe (R-AGR-3410-12-Z) at Luxembourg and Singapore Universities.
Funding
We thank Simon Campese and Nicola Turchi for pointing out an error in an earlier version. I. Nourdin was supported by the FNR grant APOGee (R-AGR-3585-10) at Luxembourg University; G. Peccati is supported by the FNR grant FoRGES (R-AGR-3376-10) at Luxembourg University; X. Yang was supported by the FNR Grant MISSILe (R-AGR-3410-12-Z) at Luxembourg and Singapore Universities.
Keywords
- Breuer–Major Theorem
- Convex distance
- Fourth moment theorems
- Gaussian fields
- Malliavin–Stein method
- Multidimensional normal approximations
ASJC Scopus subject areas
- Statistics and Probability
- General Mathematics
- Statistics, Probability and Uncertainty