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Gaussian Approximations of Multiple Integrals (2007)

Abstract
Fix k≥1, and let I(l), l ≥ 1, be a sequence of k-dimensional vectors of multiple Wiener-Itô integrals with respect to a general Gaussian process. We establish necessary and sufficient conditions to have that, as l ->+∞ , the law of I(l) is asymptotically close (for example, in the sense of Prokhorov's distance) to the law of a k-dimensional Gaussian vector having the same covariance matrix as I(l). The main feature of our results is that they require minimal assumptions (basically, boundedness of variances) on the asymptotic behaviour of the variances and covariances of the elements of I(l). In particular, we will not assume that the covariance matrix of I(l) is convergent. This generalizes the results proved in Nualart and Peccati (2005), Peccati and Tudor (2005) and Nualart and Ortiz-Latorre (2007). As shown in Marinucci and Peccati (2007b), the criteria established in this paper are crucial in the study of the high-frequency behaviour of stationary fields defined on homogeneous spaces.

Publication details
Download http://www.math.washington.edu/~ejpecp/ECP/viewarticle.php?id=1939
Publisher Institute of Mathematical Statistics
Contributors ISI Foundation -- Lagrange Project
Repository Electronic Communications in Probability (United States)
Keywords 60F05; 60G15; 60H05; 60H07; Gaussian processes; Malliavin calculus; Multiple stochastic integrals; Non-central limit theorems; Weak convergence
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