86 research outputs found

    Universality of the mean number of real zeros of random trigonometric polynomials under a weak Cramer condition

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    We investigate the mean number of real zeros over an interval [a,b][a,b] of a random trigonometric polynomial of the form ∑k=1nakcos⁡(kt)+bksin⁡(kt)\sum_{k=1}^n a_k \cos(kt)+b_k \sin(kt) where the coefficients are i.i.d. random variables. Under mild assumptions on the law of the entries, we prove that this mean number is asymptotically equivalent to n(b−a)π3\frac{n(b-a)}{\pi\sqrt{3}} as nn goes to infinity, as in the known case of standard Gaussian coefficients. Our principal requirement is a new Cramer type condition on the characteristic function of the entries which does not only hold for all continuous distributions but also for discrete ones in a generic sense. To our knowledge, this constitutes the first universality result concerning the mean number of zeros of random trigonometric polynomials. Besides, this is also the first time that one makes use of the celebrated Kac-Rice formula not only for continuous random variables as it was the case so far, but also for discrete ones. Beyond the proof of a non asymptotic version of Kac-Rice formula, our strategy consists in using suitable small ball estimates and Edgeworth expansions for the Kolmogorov metric under our new weak Cramer condition, which both constitute important byproducts of our approach

    Generalization of the Nualart-Peccati criterion

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    The celebrated Nualart-Peccati criterion [Ann. Probab. 33 (2005) 177-193] ensures the convergence in distribution toward a standard Gaussian random variable NN of a given sequence {Xn}n≥1\{X_n\}_{n\ge1} of multiple Wiener-It\^{o} integrals of fixed order, if E[Xn2]→1\mathbb {E}[X_n^2]\to1 and E[Xn4]→E[N4]=3\mathbb {E}[X_n^4]\to \mathbb {E}[N^4]=3. Since its appearance in 2005, the natural question of ascertaining which other moments can replace the fourth moment in the above criterion has remained entirely open. Based on the technique recently introduced in [J. Funct. Anal. 266 (2014) 2341-2359], we settle this problem and establish that the convergence of any even moment, greater than four, to the corresponding moment of the standard Gaussian distribution, guarantees the central convergence. As a by-product, we provide many new moment inequalities for multiple Wiener-It\^{o} integrals. For instance, if XX is a normalized multiple Wiener-It\^{o} integral of order greater than one, ∀k≥2,E[X2k]>E[N2k]=(2k−1)!!.\forall k\ge2,\qquad \mathbb {E}\bigl[X^{2k}\bigr]>\mathbb {E} \bigl[N^{2k}\bigr]=(2k-1)!!.Comment: Published at http://dx.doi.org/10.1214/14-AOP992 in the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Stein's method, Malliavin calculus, Dirichlet forms and the fourth moment theorem

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    The fourth moment theorem provides error bounds of the order E(F4)−3\sqrt{{\mathbb E}(F^4) - 3} in the central limit theorem for elements FF of Wiener chaos of any order such that E(F2)=1{\mathbb E}(F^2) = 1. It was proved by Nourdin and Peccati (2009) using Stein's method and the Malliavin calculus. It was also proved by Azmoodeh, Campese and Poly (2014) using Stein's method and Dirichlet forms. This paper is an exposition on the connections between Stein's method and the Malliavin calculus and between Stein's method and Dirichlet forms, and on how these connections are exploited in proving the fourth moment theorem

    Convergence in distribution norms in the CLT for non identical distributed random variables

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    We study the convergence in distribution norms in the Central Limit Theorem for non identical distributed random variables that is εn(f):=E(f(1n∑i=1nZi))−E(f(G))→0 \varepsilon_{n}(f):={\mathbb{E}}\Big(f\Big(\frac 1{\sqrt n}\sum_{i=1}^{n}Z_{i}\Big)\Big)-{\mathbb{E}}\big(f(G)\big)\rightarrow 0 where ZiZ_{i} are centred independent random variables and GG is a Gaussian random variable. We also consider local developments (Edgeworth expansion). This kind of results is well understood in the case of smooth test functions ff. If one deals with measurable and bounded test functions (convergence in total variation distance), a well known theorem due to Prohorov shows that some regularity condition for the law of the random variables ZiZ_{i}, i∈Ni\in {\mathbb{N}}, on hand is needed. Essentially, one needs that the law of Zi Z_{i} is locally lower bounded by the Lebesgue measure (Doeblin's condition). This topic is also widely discussed in the literature. Our main contribution is to discuss convergence in distribution norms, that is to replace the test function ff by some derivative ∂αf\partial_{\alpha }f and to obtain upper bounds for εn(∂αf)\varepsilon_{n}(\partial_{\alpha }f) in terms of the infinite norm of ff. Some applications are also discussed: an invariance principle for the occupation time for random walks, small balls estimates and expected value of the number of roots of trigonometric polynomials with random coefficients

    Fourth Moment Theorems for Markov Diffusion Generators

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    Inspired by the insightful article arXiv:1210.7587, we revisit the Nualart-Peccati-criterion arXiv:math/0503598 (now known as the Fourth Moment Theorem) from the point of view of spectral theory of general Markov diffusion generators. We are not only able to drastically simplify all of its previous proofs, but also to provide new settings of diffusive generators (Laguerre, Jacobi) where such a criterion holds. Convergence towards gamma and beta distributions under moment conditions is also discussed.Comment: 15 page

    Classical and free Fourth Moment Theorems: universality and thresholds

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    Let XX be a centered random variable with unit variance, zero third moment, and such that E[X4]≥3E[X^4] \ge 3. Let {Fn:n≥1}\{F_n : n\geq 1\} denote a normalized sequence of homogeneous sums of fixed degree d≥2d\geq 2, built from independent copies of XX. Under these minimal conditions, we prove that FnF_n converges in distribution to a standard Gaussian random variable if and only if the corresponding sequence of fourth moments converges to 33. The statement is then extended (mutatis mutandis) to the free probability setting. We shall also discuss the optimality of our conditions in terms of explicit thresholds, as well as establish several connections with the so-called universality phenomenon of probability theory. Both in the classical and free probability frameworks, our results extend and unify previous Fourth Moment Theorems for Gaussian and semicircular approximations. Our techniques are based on a fine combinatorial analysis of higher moments for homogeneous sums.Comment: 26 page

    Stein's method on the second Wiener chaos : 2-Wasserstein distance

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    In the first part of the paper we use a new Fourier technique to obtain a Stein characterizations for random variables in the second Wiener chaos. We provide the connection between this result and similar conclusions that can be derived using Malliavin calculus. We also introduce a new form of discrepancy which we use, in the second part of the paper, to provide bounds on the 2-Wasserstein distance between linear combinations of independent centered random variables. Our method of proof is entirely original. In particular it does not rely on estimation of bounds on solutions of the so-called Stein equations at the heart of Stein's method. We provide several applications, and discuss comparison with recent similar results on the same topic
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