Asymptotic behavior of a tagged particle in simple exclusion processes

We review in this article central limit theorems for a tagged particle in the simple exclusion process. In the first two sections we present a general method to prove central limit theorems for additive functional of Markov processes. These results are then applied to the case of a tagged particle in the exclusion process. Related questions, such as smoothness of the diffusion coefficient and finite dimensional approximations, are considered in the last section.     

Limit theorems for partially hyperbolic systems

We consider a large class of partially hyperbolic systems containing, among others, affine maps, frame flows on negatively curved manifolds, and mostly contracting diffeomorphisms. If the rate of mixing is sufficiently high, the system satisfies many classical limit theorems of probability theory.

     

Limit theorems for the maximal eigenvalues of the mean-field Hamiltonian with random potential

Let , be the mean-field Hamiltonian with and random i.i.d. potential ξ_V. We prove limit theorems for the extreme eigenvalues of as |V|→∞. The limiting distributions are the same as for the corresponding extremes of ξ_V only if either (i) ξ_V is undbounded and , or (ii) ξ_V is bounded with “sharp” peaks and . Localization properties for the corresponding eigenfunctions are also studied. Institute of Mathematics and Informatics, Akademijos 4, 2600 Vilnius, Lithuania. Published in Lietuvos Matematikos Rinkinys, Vol. 39, No. 2, pp. 147–168, April–June, 1999.     

Limit theorems for random walks

We consider a random walk $S_{\tau }$ which is obtained from the simple random walk $S$ by a discrete time version of Bochner’s subordination. We prove that under certain conditions on the subordinator $\tau$ appropriately scaled random walk $S_{\tau }$ converges in the Skorohod space to the symmetric $\alpha$-stable process $B^{\alpha }$. We also prove asymptotic formula for the transition function of $S_{\tau }$ similar to the Pólya’s asymptotic formula for $B^{\alpha }$.     

Limit theorems for stationary random evolutions

On a separable Banach space, let A(ξ₁),A(ξ₂),... be a strictly stationary sequence of infinitesimal operators, centered so that EA(ξ_i) = 0, i = 1,2,.... This paper characterizes the limit of the random evolutions

as the solution to a martingale problem. This work is a direct extension of previous work on i.i.d. random evolutions.     

Second order limit theorems for the Markov branching process in random environments

In a Markov branching process with random environments, limiting fluctuations of the population size arise from the changing environment, which causes random variation of the ‘deterministic’ population prediction, and from the stochastic wobble around this ‘deterministic’ mean, which is apparent in the ordinary Markov branching process. If the random environment is generated by a suitable stationary process, the first variation typically swamps the second kind. In this paper, environmental processes are considered which, in contrast, lead to sampling and environmental fluctuation of comparable magnitude. The method makes little use either of stationarity or of the branching property, and is amenable to some generalization away from the Markov branching process.     

Limit theorems for Hilbert space-valued linear processes under long range dependence

Let ${\left(X_k\right)}_{k\in \mathbb{Z}}$ be a linear process with values in a separable Hilbert space $\mathbb{H}$ given by $X_k={\sum }_{j=0}^{\infty }{(j+1)}^{?N}{\epsilon }_{k?j}$ for each $k\in \mathbb{Z}$, where $N:\mathbb{H}\to \mathbb{H}$ is a bounded, linear normal operator and ${\left({\epsilon }_k\right)}_{k\in \mathbb{Z}}$ is a sequence of independent, identically distributed $\mathbb{H}$-valued random variables with $E{\epsilon }_0=0$ and $E6{\epsilon }_0{6}^2<\infty$. We investigate the central and the functional central limit theorem for ${\left(X_k\right)}_{k\in \mathbb{Z}}$ when the series of operator norms ${\sum }_{j=0}^{\infty }6{(j+1)}^{?N}{6}_{op}$ diverges. Furthermore, we show that the limit process in case of the functional central limit theorem generates an operator self-similar process.     

Limit theorems in the context of multivariate long-range dependence

This article considers multivariate linear processes whose components are either short- or long-range dependent. The functional central limit theorems for the sample mean and the sample autocovariances for these processes are investigated, paying special attention to the mixed cases of short- and long-range dependent series. The resulting limit processes can involve multivariate Brownian motion marginals, operator fractional Brownian motions and matrix-valued versions of the so-called Rosenblatt process.     

Limit theorems for random permanents with exchangeable structure

Permanents of random matrices extend the concept of U-statistics with product kernels. In this paper, we study limiting behavior of permanents of random matrices with independent columns of exchangeable components. Our main results provide a general framework which unifies already existing asymptotic theory for projection matrices as well as matrices of all-iid entries. The method of the proofs is based on a Hoeffding-type orthogonal decomposition of a random permanent function. The decomposition allows us to relate asymptotic behavior of permanents to that of elementary symmetric polynomials based on triangular arrays of rowwise independent rv's.     

Small time central limit theorems for semimartingales with applications

We give conditions under which the normalized marginal distribution of a semimartingale converges to a Gaussian limit law as time tends to zero. In particular, our result is applicable to solutions of stochastic differential equations with locally bounded and continuous coefficients. The limit theorems are subsequently extended to functional central limit theorems on the process level. We present two applications of the results in the field of mathematical finance: to the pricing of at-the-money digital options with short maturities and short time implied volatility skews.     

Almost sure central limit theorems for random fields

A general almost sure limit theorem is presented for random fields. It is applied to obtain almost sure versions of some (functional) central limit theorems. (© 2003 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Limit theorems for random transformations and processes in random environments

I derive general relativized central limit theorems and laws of iterated logarithm for random transformations both via certain mixing assumptions and via the martingale differences approach. The results are applied to Markov chains in random environments, random subshifts of finite type, and random expanding in average transformations where I show that the conditions of the general theorems are satisfied and so the corresponding (fiberwise) central limit theorems and laws of iterated logarithm hold true in these cases. I consider also a continuous time version of such limit theorems for random suspensions which are continuous time random dynamical systems.

     

Limit theorems for a class of diffusion processes

We consider a diffusion process {x(t)} on a compact Riemannian manifold with generator δ/2 + b. A current‐valued continuous stochastic process {X _t} in the sense of Itô [8] corresponds to {x(t)} by considering the stochastic line integral X _t(a) along {x(t)} for every smooth 1-form a. Furthermore {X _t} is decomposed into the martingale part and the bounded variation part as a current-valued continuous process. We show the central limit theorems for {X _t} and the martingale part of {X _t}. Occupation time laws for recurrent diffusions and homogenization problems of periodic diffusions are closely related to these theorems     

Central limit theorems for the large-spin asymptotics of quantum spins

We use a generalized form of Dysons spin wave formalism to prove several central limit theorems for the large-spin asymptotics of quantum spins in a coherent state.T. Michoel is a Postdoctoral Fellow of the Fund for Scientific Research – Flanders (Belgium) (F.W.O.–Vlaanderen)This material is based on work supported by the National Science Foundation under Grant No. DMS0303316.This article may be reproduced in its entirety for non-commercial purposes.Mathematics Subject Classification (2000): 60F05, 82B10, 82B24, 82D40     

Limit theorems for the maximum likelihood estimate under general multiply Type II censoring

Assume n items are put on a life-time test, however for various reasons we have only observed the r ₁-th,..., r _k-th failure times % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiiYdd9qrFfea0dXdf9vqai-hEir8Ve% ea0de9qq-hbrpepeea0db9q8as0-LqLs-Jirpepeea0-as0Fb9pgea% 0lrP0xe9Fve9Fve9qapdbaqaaeGacaGaaiaabeqaamaabaabcaGcba% GaamiEamaaBaaaleaamiaadkhadaWgaaqaaSGaaGymaiaacYcaaWqa% baGaamOBaiaacYcacaGGUaGaaiOlaiaac6caaSqabaGccaGGSaGaam% iEamaaBaaaleaamiaadkhadaWgaaqaaSGaam4AaiaacYcaaWqabaGa% amOBaaWcbeaaaaa!48BB!\[x_{r_{1,} n,...} ,x_{r_{k,} n} \]with % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiiYdd9qrFfea0dXdf9vqai-hEir8Ve% ea0de9qq-hbrpepeea0db9q8as0-LqLs-Jirpepeea0-as0Fb9pgea% 0lrP0xe9Fve9Fve9qapdbaqaaeGacaGaaiaabeqaamaabaabcaGcba% GaaGimaiabgsMiJkaadIhadaWgaaWcbaadcaWGYbWaaSbaaeaaliaa% igdacaGGSaaameqaaiaad6gaaSqabaGccqGHKjYOcqWIVlctcqGHKj% YOcaWG4bWaaSbaaSqaaWGaamOCamaaBaaabaWccaWGRbGaaiilaaad% beaacaWGUbaaleqaaeXatLxBI9gBaGqbaOGae8hpaWJaeyOhIukaaa!521B!\[0 \le x_{r_{1,} n} \le \cdots \le x_{r_{k,} n} > \infty \]. This is a multiply Type II censored sample. A special case where each x _ri ,n goes to a particular percentile of the population has been studied by various authors. But for the general situation where the number of gaps as well as the number of unobserved values in some gaps goes to , the asymptotic properties of MLE are still not clear. In this paper, we derive the conditions under which the maximum likelihood estimate of is consistent, asymptotically normal and efficient. As examples, we show that Weibull distribution, Gamma and Logistic distributions all satisfy these conditions.This research was supported in part by the Designated Research Initiative Fund, University of Maryland Baltimore County.     

Limit theorems for the convex hull of random points in higher dimensions

We give a central limit theorem for the number of vertices of the convex hull of independent and identically distributed random vectors, being sampled from a certain class of spherically symmetric distributions in that includes the normal family. Furthermore, we prove that, among these distributions, the variance of exhibits the same order of magnitude as the expectation as The main tools are Poisson approximation of the point process of vertices of the convex hull and (sub/super)-martingales.

     

Multivariate Variational Inequalities and the Central Limit Theorem

Multivariate variational inequalities are obtained in terms of thew-functions and the trace of a Fisher-type information matrix. In consequence of these inequalities, the multivariate central limit theorem arises in the sense of the total variation.     

Limit theorems for sums of random exponentials

We study limiting distributions of exponential sums as t→∞, N→∞, where (X_i) are i.i.d. random variables. Two cases are considered: (A) ess sup X_i = 0 and (B) ess sup X_i = ∞. We assume that the function h(x)= -log P{X_i>x} (case B) or h(x) = -log P {X_i>-1/x} (case A) is regularly varying at ∞ with index 1 < ϱ <∞ (case B) or 0 < ϱ < ∞ (case A). The appropriate growth scale of N relative to t is of the form , where the rate function H₀(t) is a certain asymptotic version of the function (case B) or (case A). We have found two critical points, λ₁<λ₂, below which the Law of Large Numbers and the Central Limit Theorem, respectively, break down. For 0 < λ < λ₂, under the slightly stronger condition of normalized regular variation of h we prove that the limit laws are stable, with characteristic exponent α = α (ϱ, λ) ∈ (0,2) and skewness parameter β ≡ 1.Research supported in part by the DFG grants 436 RUS 113/534 and 436 RUS 113/722.Mathematics Subject Classification (2000): 60G50, 60F05, 60E07