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Mod-ϕ Convergence : normality zones and precise deviations


Féray, Valentin; Méliot, Pierre-Loïc; Nikeghbali, Ashkan Mod-ϕ Convergence : normality zones and precise deviations. Edited by: Féray, Valentin; Méliot, Pierre-Loïc; Nikeghbali, Ashkan (2016). Cham, Switzerland: Springer.

Abstract

In this paper, we use the framework of mod-$\phi$ convergence to prove precise large or moderate deviations for quite general sequences of random variables ($X_n$)$ _{n\in{\mathbb{N}}}$. The random variables considered can be lattice or non-lattice distributed, and single or multi-dimensional; and one obtains precise estimates of the fluctuations $\mathbb{P}[X_{n} \in t_{n}B]$, instead of the usual estimates for the rate of exponential decay log($\mathbb{P}[X_{n} \in t_{n}B]$In the special setting of mod-Gaussian convergence, we shall see that our approach allows us to identify the scale at which the central limit theorem ceases to hold and we are able to quantify the "breaking of symmetry" at this critical scale thanks to the residue or limiting function occurring in mod-f convergence. In particular this provides us with a systematic way to characterise the normality zone, that is the zone in which the Gaussian approximation for the tails is still valid. Besides, the residue function measures the extent to which this approximation fails to hold at the edge of the normality zone.
The first sections of the article are devoted to a proof of these abstract results. We then propose new examples covered by this theory and coming from various areas of mathematics: classical probability theory (multi-dimensional random walks, random point processes), number theory (statistics of additive arithmetic functions), combinatorics (statistics of random permutations), random matrix theory (characteristic polynomials of random matrices in compact Lie groups), graph theory (number of subgraphs in a random Erd˝os-Rényi graph), and non-commutative probability theory (asymptotics of random character values of symmetric groups). In particular, we complete our theory of precise deviations by a concrete method of cumulants and dependency graphs, which applies to many examples of sums of “weakly dependent” random variables. Although the latter methods can only be applied in the more restrictive setting of mod-Gaussian convergence, the large number as well as the variety of examples which are covered there hint at a universality class for second order fluctuations.

Abstract

In this paper, we use the framework of mod-$\phi$ convergence to prove precise large or moderate deviations for quite general sequences of random variables ($X_n$)$ _{n\in{\mathbb{N}}}$. The random variables considered can be lattice or non-lattice distributed, and single or multi-dimensional; and one obtains precise estimates of the fluctuations $\mathbb{P}[X_{n} \in t_{n}B]$, instead of the usual estimates for the rate of exponential decay log($\mathbb{P}[X_{n} \in t_{n}B]$In the special setting of mod-Gaussian convergence, we shall see that our approach allows us to identify the scale at which the central limit theorem ceases to hold and we are able to quantify the "breaking of symmetry" at this critical scale thanks to the residue or limiting function occurring in mod-f convergence. In particular this provides us with a systematic way to characterise the normality zone, that is the zone in which the Gaussian approximation for the tails is still valid. Besides, the residue function measures the extent to which this approximation fails to hold at the edge of the normality zone.
The first sections of the article are devoted to a proof of these abstract results. We then propose new examples covered by this theory and coming from various areas of mathematics: classical probability theory (multi-dimensional random walks, random point processes), number theory (statistics of additive arithmetic functions), combinatorics (statistics of random permutations), random matrix theory (characteristic polynomials of random matrices in compact Lie groups), graph theory (number of subgraphs in a random Erd˝os-Rényi graph), and non-commutative probability theory (asymptotics of random character values of symmetric groups). In particular, we complete our theory of precise deviations by a concrete method of cumulants and dependency graphs, which applies to many examples of sums of “weakly dependent” random variables. Although the latter methods can only be applied in the more restrictive setting of mod-Gaussian convergence, the large number as well as the variety of examples which are covered there hint at a universality class for second order fluctuations.

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Additional indexing

Item Type:Edited Scientific Work
Communities & Collections:07 Faculty of Science > Institute of Mathematics
Dewey Decimal Classification:510 Mathematics
Language:English
Date:2016
Deposited On:19 Jan 2017 08:25
Last Modified:19 Jan 2017 08:28
Publisher:Springer
Series Name:Springer briefs in probability and mathematical statistics
Number of Pages:152
ISSN:2365-4333
ISBN:978-3-319-46821-1
Publisher DOI:https://doi.org/10.1007/978-3-319-46822-8
Related URLs:http://link.springer.com/book/10.1007%2F978-3-319-46822-8 (Publisher)

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