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We compute explicit bounds in the normal and chi-square approximations of multilinear homogenous sums (of arbitrary order) of general centered independent random variables with unit variance. In particular, we show that chaotic random variables enjoy the following form of universality: (a) the normal and chi-square approximations of any homogenous sum can be completely characterized and assessed by first switching to its Wiener chaos counterpart, and (b) the simple upper bounds and convergence criteria available on the Wiener chaos extend almost verbatim to the class of homogeneous sums. . This reprint differs from the original in pagination and typographic detail. 1 2 I. NOURDIN, G. PECCATI AND G. REINERTOur findings partially rely on the notion of "low influences" (see again [10]) for real-valued functions defined on product spaces. As indicated by the title, we regard the two properties (a) and (b) as an instance of the universality phenomenon, according to which most information about large random systems (such as the "distance to Gaussian" of nonlinear functionals of large samples of independent random variables) does not depend on the particular distribution of the components. Other recent examples of the universality phenomenon appear in the already quoted paper [10], as well as in the Tao-Vu proof of the circular law for random matrices, as detailed in [31] (see also the Appendix to [31] by Krishnapur). Observe that, in Section 7, we will prove analogous results for the multivariate normal approximation of vectors of homogenous sums of possibly different orders. In a further work by the first two authors (see [14]) the results of the present paper are applied in order to deduce universal Gaussian fluctuations for traces associated with non-Hermitian matrix ensembles.

We show how to use the Malliavin calculus to obtain a new exact formula for the density ρ of the law of any random variable Z which is measurable and dierentiable with respect to a given isonormal Gaussian process. The main advantage of this formula is that it does not refer to the divergence operator (dual of the Malliavin derivative). In particular, density lower bounds can be obtained in some instances. Among several examples, we provide an application to the (centered) maximum of a general Gaussian process, thus extending a formula recently used by Chatterjee [4]. We also explain how to derive concentration inequalities for Z in our framework.

Stein's method is a collection of probabilistic techniques that allow one to assess the distance between two probability distributions by means of differential operators. In 2007, the authors discovered that one can combine Stein's method with the powerful Malliavin calculus of variations, in order to deduce quantitative central limit theorems involving functionals of general Gaussian fields. This book provides an ideal introduction both to Stein's method and Malliavin calculus, from the standpoint of normal approximations on a Gaussian space. Many recent developments and applications are studied in detail, for instance: fourth moment theorems on the Wiener chaos, density estimates, Breuer–Major theorems for fractional processes, recursive cumulant computations, optimal rates and universality results for homogeneous sums. Largely self-contained, the book is perfect for self-study. It will appeal to researchers and graduate students in probability and statistics, especially those who wish to understand the connections between Stein's method and Malliavin calculus.

We combine Stein's method with Malliavin calculus in order to obtain explicit bounds in the multidimensional normal approximation (in the Wasserstein distance) of functionals of Gaussian fields. Our results generalize and refine the main findings by Peccati and Tudor (2005), Nualart and Ortiz-Latorre (2007), Peccati (2007) and Peccati (2007b, 2008); in particular, they apply to approximations by means of Gaussian vectors with an arbitrary, positive definite covariance matrix. Among several examples, we provide an application to a functional version of the Breuer-Major CLT for fields subordinated to a fractional Brownian motion.

We develop connections between Stein's approximation method, logarithmic Sobolev and transport inequalities by introducing a new class of functional inequalities involving the relative entropy, the Stein kernel, the relative Fisher information and the Wasserstein distance with respect to a given reference distribution on R d . For the Gaussian model, the results improve upon the classical logarithmic Sobolev inequality and the Talagrand quadratic transportation cost inequality. Further examples of illustrations include multidimensional gamma distributions, beta distributions, as well as families of log-concave densities. As a by-product, the new inequalities are shown to be relevant towards convergence to equilibrium, concentration inequalities and entropic convergence expressed in terms of the Stein kernel. The tools rely on semigroup interpolation and bounds, in particular by means of the iterated gradients of the Markov generator with invariant measure the distribution under consideration. In a second part, motivated by the recent investigation by Nourdin, Peccati and Swan on Wiener chaoses, we address the issue of entropic bounds on multidimensional functionals F with the Stein kernel via a set of data on F and its gradients rather than on the Fisher information of the density. A natural framework for this investigation is given by the Markov Triple structure (E, µ, Γ) in which abstract Malliavin-type arguments may be developed and extend the Wiener chaos setting.

Given an integer m, a probability measure ν on [0, 1], a process X and a real function g, we define the m-order ν-integral having as integrator X and as integrand g(X). In the case of the fractional Brownian motion B H , for any locally bounded function g, the corresponding integral vanishes for all odd indices m >

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