almost sure limit theorems

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On strong and almost sure local limit theorems for a probabilistic model of the Dickman distribution

On strong and almost sure local limit theorems for a probabilistic model of the Dickman distribution

P(Z k = 1) = 1/k = 1 − P(Z k = 0) (k > 1) and put T n := P 16k6n kZ k . It is then known that T n /n converges weakly to a real random variable D with density proportional to the Dickman function, defined by the delay-differential equation u% 0 (u) + %(u − 1) = 0 (u > 1) with initial condition %(u) = 1 (0 6 u 6 1). Improving on earlier work, we propose asymptotic formulae with remainders for the corresponding local and almost sure limit theorems, namely

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Variations around Eagleson's Theorem on mixing limit theorems for dynamical systems

Variations around Eagleson's Theorem on mixing limit theorems for dynamical systems

1. Almost sure limit theorems In this section, we discuss a version of Eagleson’s result that applies to almost sure limit theorems. Given two probability measures m 1 and m 2 , the goal will be to construct a coupling between these two measures that respects the orbit structure of the space, as in [Kor17]. Then, it will readily follow that an almost sure limit theorem with respect to m 1 implies one with respect to m 2 . Our argument works for general maps but, contrary

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A moment approach for the almost sure central limit theorem for martingales

A moment approach for the almost sure central limit theorem for martingales

[13] Lai T. L., Wei C. Z., 1982. Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems. Ann. Statist. 10, 154-166. [14] Lifshits M., 2002. Almost sure limit theorem for martingales. Limit Theorems in Probability and Statistics II, (I. Berkes, E. Cs´ aki, M. Cs¨ org˝ o, eds.) J. Bolyai Mathematical Society, Budapest, 367-390.

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Moment bounds and central limit theorems for Gaussian subordinated arrays

Moment bounds and central limit theorems for Gaussian subordinated arrays

This paper is devoted to the proof of two new results concerning functions of Gaussian vectors. The first one (Lemma 1 of Section 2) is a moment bound for “off-diagonal” sums of products of functions of Gaussian vectors in a general frame. It is an extension of an important lemma by Taqqu (1977, Lemma 4.5). This result is useful for obtaining almost sure convergence and tightness of Gaussian subordinated functionals and statistics, see Remark 1 below. The proof of Lemma 1 uses the Hermitian decomposition of L 2 function

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Limit theorems for spatio-temporal models with long-range dependence

Limit theorems for spatio-temporal models with long-range dependence

The results of this chapter are related to those in, e.g. [ 11 , 27 , 35 , 53 , 55 , 70 , 79 , 80 , 89 , 90 ] in which different scaling regimes occur for various classes of LRD models, in particular, heavy- tailed duration models. Isotropic scaling limits (case γ = 1) of random grain and random balls models in arbitrary dimension were discussed in Kaj et al. [ 53 ] and Biermé et al. [ 11 ]. The monograph [ 69 ] provides a nice discussion of limit behavior of heavy-tailed duration models whose spatial version is the random grain model in ( 7.2 ). From an application view- point, probably the most interesting is the study of different scaling regimes of superposed network traffic models [ 27 , 35 , 55 , 70 ]. In these studies, it is assumed that traffic is generated by independent sources and the problem concerns the limit distribution of the aggregated traffic as the time scale T and the number of sources M both tend to infinity, possibly at different rate. The present chapter extends the above-mentioned work, by considering the limit behavior of the aggregated workload process:
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Local limit theorems in relatively hyperbolic groups I : rough estimates

Local limit theorems in relatively hyperbolic groups I : rough estimates

As we saw, free products provide a great source of examples for local limit theorems. There are several equivalent ways of defining relatively hyperbolic groups (see Section 2 for more details). If Ω is a collection of subgroups of Γ, we say that Γ is hyperbolic relative to Ω if it acts geometrically finitely on a proper geodesic hyperbolic space X such that the stabilizers of the parabolic limit points are exactly the subgroups in Ω. The elements of Ω are called peripheral subgroups or (maximal) parabolic subgroups. We fix a collection Ω0 of representatives of conjugacy classes of Ω. According to [7, Proposition 6.15], such a collection is finite. If Γ is a free product of the form Γ = Γ1 ∗ ... ∗ Γn, then Γ is relatively hyperbolic with respect to conjugacy classes of the free factors Γi and one can choose Ω0 = {Γ1, ..., Γn}.
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Bounds on Regeneration Times and Limit Theorems for Subgeometric Markov Chains

Bounds on Regeneration Times and Limit Theorems for Subgeometric Markov Chains

k=0 r(k)f (X k )] is not always easy, we will consider bounds for this quantity derived from a ”subgeometric” condition re- cently introduced in Douc et al. (2004), which might be seen, in the subgeomet- rical case, as an analog to the Foster-Lyapunov drift condition for geometrically ergodic Markov Chains. We obtain, using these drift conditions, explicit bounds for the (f, r)-modulated expectation of moments of the regeneration times in terms of the constants in A1, the sequence r and the constants appearing in the drift conditions. With these results, we obtain limit theorems for addi- tive functionals and deviations inequalities, under conditions which are easy to check.
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Central Limit Theorems and Quadratic Variations in terms of Spectral Density

Central Limit Theorems and Quadratic Variations in terms of Spectral Density

OF SPECTRAL DENSITY HERMINE BIERM´ E, ALINE BONAMI, AND JOS´ E R. LE ´ ON Abstract. We give a new proof and provide new bounds for the speed of convergence in the Central Limit Theorems of Breuer Major on stationary Gaussian time series, which generalizes to particular triangular arrays. Our assumptions are given in terms of the spectral density of the time series. We then consider generalized quadratic variations of Gaussian fields with stationary increments under the assumption that their spectral density is asymptotically self-similar and prove Central Limit Theorems in this context.
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Limit theorems for additive functionals of a Markov chain

Limit theorems for additive functionals of a Markov chain

ond moment, one expects that the central limit theorem holds for N −1/2 S N , as N → +∞. This, of course, requires some assumptions about the rate of the decay of correlations of the chain, as well as hypotheses about its dy- namics. If Ψ has an infinite second moment and its tails satisfy a power law, then one expects, again under some assumption on the transition prob- abilities, convergence of the laws of N −1/α S N , for an appropriate α to the

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Central Limit Theorems for arrays of decimated linear processes

Central Limit Theorems for arrays of decimated linear processes

[2] R. J. Bhansali, L. Giraitis, and P. S. Kokoszka. Approximations and limit theory for quadratic forms of linear processes. Stochastic Process. Appl., 117(1):71–95, 2007. [3] P. Billingsley. Convergence of probability measures. Wiley Series in Probability and Statistics: Probability and Statistics. John Wiley & Sons Inc., New York, second edition, 1999. A Wiley-Interscience Publication.

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Limit theorems for some branching measure-valued processes

Limit theorems for some branching measure-valued processes

 2E[(τ Hx + 1)f (Hx)] τ x + 2 − f(x)  . 3.2. Many-to-one formulas. In order to compute our limit theorem, we need to control the second moment. As in [5], we begin by describing the population over the whole tree. Then we give a many-to-one formula for forks. Let T be the random set representing cells that have lived at a certain moment. It is defined by

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Probabilistic modeling in finance and biology - Limit theorems and applications

Probabilistic modeling in finance and biology - Limit theorems and applications

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r]

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Limit theorems for some adaptive MCMC algorithms with subgeometric kernels

Limit theorems for some adaptive MCMC algorithms with subgeometric kernels

H l (θ, x) = θ + γ l H(θ, x). In this latter case, it is assumed that the best values for θ are the solutions of the equation R H(θ, x)π(dx) = 0. Since the pioneer work of Gilks et al. (1998); Holden (1998); Haario et al. (2001); Andrieu and Robert (2001), the number of AMCMC algorithms in the literature has significantly increased in recent years. But de- spite many recent works on the topic, the asymptotic behavior of these algorithms is still not completely understood. Almost all previous works on the convergence of AMCMC are limited to the case when each kernel P θ is geometrically ergodic (see e.g.. Roberts and
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Central Limit Theorems for the Brownian motion on large unitary groups

Central Limit Theorems for the Brownian motion on large unitary groups

[MSS07] J.A. Mingo, P. ´ Sniady, R. Speicher Second order freeness and fluctuations of random matrices. II. Unitary random matrices. Adv. Math. 209 (2007), no. 1, 212–240. [PSV77] G. C. Papanicolaou, D. Stroock, S. R. S. Varadhan Martingale approach to some limit theorems. Papers from the Duke Turbulence Conference (Duke Univ., Durham, N.C., 1976), Paper No. 6, Duke Univ. Math. Ser., Vol. III, Duke Univ., Durham, N.C., 1977.

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Central Limit Theorems for Linear Statistics of Heavy Tailed Random Matrices

Central Limit Theorems for Linear Statistics of Heavy Tailed Random Matrices

Note that the speed of the central limit theorem is N −1/2 as for independent integrable random variables, but differently from what happens for standard Wigner’s matrices. This phenomenon has also already been observed for adjacency matrix of random graphs [9, 24] and we will see below that it also holds for L´evy matrices. It suggests that the repulsive interactions exhibited by the eigenvalues of most models of random matrices with lighter tails than heavy tailed matrices no longer work here.

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Central Limit Theorems for Stochastic Approximation with controlled Markov chain dynamics

Central Limit Theorems for Stochastic Approximation with controlled Markov chain dynamics

2 A Central Limit Theorem for Stochastic Approximation 8 θ ⋆ | ≤ δ}. In most contributions, rates of convergence are derived under the con- dition (7) (see e.g. the recent works by Pelletier [23] and Lelong [21]). This framework is too restrictive to address the case of SA with controlled Markov chain dynamics when the ergodic properties of the transition kernels {Q θ , θ ∈ Θ}

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An almost-sure estimate for the mean of generalized $Q$-multiplicative functions of modulus $1$

An almost-sure estimate for the mean of generalized $Q$-multiplicative functions of modulus $1$

given by ajQj, the compact group ZQ being endowed with its normalized Haar measure M, and clearly. In this article, we show roughly speaking that although the relatio[r]

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On limit theorems and backward stochastic differential equations via Malliavin calculus

On limit theorems and backward stochastic differential equations via Malliavin calculus

Dans cette partie de notre travail, nous mettons en oeuvre les techniques du calcul de Malliavin combinées à la méthode de Stein afin de determiner, dans un cadre gaussien, des bornes de [r]

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Limit theorems for extreme value estimates of point processes boundaries

Limit theorems for extreme value estimates of point processes boundaries

Unité de recherche INRIA Rhône-Alpes 655, avenue de l’Europe - 38330 Montbonnot-St-Martin France Unité de recherche INRIA Lorraine : LORIA, Technopôle de Nancy-Brabois - Campus scientifi[r]

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Growth of normalizing sequences in limit theorems for conservative maps

Growth of normalizing sequences in limit theorems for conservative maps

α = 2 and L(n) = 1, this is an instance of the classical central limit theorem. (2) When T preserves m and m has infinite mass, one obtains different limits. Let α ∈ [0, 1) (we exclude α = 1 to avoid degenerate limit laws). The Darling-Kac theorem gives examples of transformations T such that, for any function f which is integrable and has non-zero average for m, then S n f /(n α L(n)) converges in distribution with

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