Gaussian approximations of multiple integrals

被引:7
|
作者
Peccati, Giovanni [1 ]
机构
[1] Univ Paris 06, Lab Probab & Modeles Aleatoires, F-75252 Paris 05, France
关键词
Gaussian processes; Malliavin calculus; Multiple stochastic integrals; Non-central limit theorems; Weak convergence;
D O I
10.1214/ECP.v12-1322
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Fix k >= 1, and let I(l), l >= 1, be a sequence of k-dimensional vectors of multiple Wiener-It (o) over cap integrals with respect to a general Gaussian process. We establish necessary and sufficient conditions to have that, as l -> +infinity, 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 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-Lattore (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.
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页码:350 / 364
页数:15
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