一类随机变量序列部分和的大偏差

设{xn，n≥1}是鞅差序列，Sn=∑i=1^n Xi，Xi∈L^p，i≥1，文章研究了混合序列和M-Z序列部分和Sn的大偏差，并得到了和鞅差序列类似的结果．     

一类负相伴随机阵列部分和的精致大偏差

本文在一些适当的条件下得到了多风险模型中负相伴随机阵列的精致大偏差,推广了一些已知的结果,同时表明在多风险模型中负相伴结构对精致大偏差同样不具有敏感性.     

Large Deviations for Intersections of Random Walks

We prove a large deviations principle for the number of intersections of two independent infinite-time ranges in dimension 5 and greater, improving upon the moment bounds of Khanin, Mazel, Shlosman, and Sinaï [9]. This settles, in the discrete setting, a conjecture of van den Berg, Bolthausen, and den Hollander [15], who analyzed this question for the Wiener sausage in the finite-time horizon. The proof builds on their result (which was adapted in the discrete setting by Phetpradap [12]), and combines it with a series of tools that were developed in recent works of the authors [2, 3, 5]. Moreover, we show that most of the intersection occurs in a single box where both walks realize an occupation density of order 1. © 2022 Wiley Periodicals, Inc.     

经验测度序列的大偏差原理

设{Xi,I∈N)是平稳NA随机变量序列且ψ-(1)＞0.记经验测度δn=1/n∑I=1δxi,n≥1,借助于弱收敛拓扑下的开集与β度量下的开球之间的关系,证明了{P{δn∈·},n→∞}在(M1(R),ω→)上满足大偏差原理.     

经验测度序列的大偏差原理

设{X_i,i∈N)是平稳N A随机变量序列且ψ_(1)＞0．记经验测度δ_n=1/n■,借助于弱收敛拓扑下的开集与β度量下的开球之间的关系,证明了{P{δ_n∈·},n→∞}在(M_1(R),■)上满足大偏差原理．     

Upper Large Deviations for Mixing Random Sequence

In this article, we prove upper large deviations for the empirical measure generated by stationary mixing random sequence under some suitable assumptions and upper large deviations for the mixing random sequence.     

Large Deviations for Quadratic Forms of Locally Stationary Processes

We are interested in large deviations for consistent statistics which are quadratic forms of Gaussian locally stationary processes in the sense of Dahlhaus.     

重尾随机变量和的大偏差

This paper is a further investigation of large deviations for sums of random variables S_n=sum form i=1 to n X_i and S(t)=sum form i=1 to N(t) X_i,(t≥0), where {X_n,n≥1) are independent identically distribution and non-negative random variables, and {N(t),t≥0} is a counting process of non-negative integer-valued random variables, independent of {X_n,n≥1}. In this paper, under the suppose F∈G, which is a bigger heavy-tailed class than C, proved large deviation results for sums of random variables.     

Quenched Large Deviations for Multidimensional Random Walk in Random Environment with Holding Times

We consider a random walk in random environment with random holding times, that is, the random walk jumping to one of its nearest neighbors with some transition probability after a random holding time. Both the transition probabilities and the laws of the holding times are randomly distributed over the integer lattice. Our main result is a quenched large deviation principle for the position of the random walk. The rate function is given by the Legendre transform of the so-called Lyapunov exponents for the Laplace transform of the first passage time. By using this representation, we derive some asymptotics of the rate function in some special cases.     

Large Deviations View Points for Heavy-Tailed Random Walks

Let X ₁, X ₂,... be a sequence of i.i.d. non-negative random variables with heavy tails. W e study logarithmic asymptotics for the distributions of the partial sums S _n = X ₁ + ··· + X _n . Our main interest is in the crude estimates P(S _n > n ^x ) n ^{–x + 1} for appropriate values of x where is a specific parameter. The related conjecture proposed by Gantert (Stat. Probab. Lett. 49, 113–118) is investigated.     

Large Deviations for Sums of Independent Heavy-Tailed Random Variables

We obtain precise large deviations for heavy-tailed random sums , of independent random variables. are nonnegative integer-valued random variables independent of r.v. (X _i )i N with distribution functions F _i. We assume that the average of right tails of distribution functions F _i is equivalent to some distribution function with regularly varying tail. An example with the Pareto law as the limit function is given.     

Large Deviations in Generalized Random Graphs with Node Weights

Generalized random graphs are considered where the presence or absence of an edge depends on the weights of its nodes. Our main interest is to investigate large deviations for the number of edges per node in such a generalized random graph, where the node weights are deterministic under some regularity conditions, as well as chosen i.i.d. from a finite set with positive components. When the node weights are random variables, obstacles arise because the independence among edges no longer exists, our main tools are some results of large deviations for mixtures. After calculating, our results show that the corresponding rate functions for the deterministic case and the random case are very different.     

特殊重尾随机变量随机和的大偏差

文献[1]对于一些经典重尾随机变量的随机和大偏差作了有意义的讨论,本文则讨论了另外一些同样有用的重尾随机和的大偏差.     

Large Deviations for Heavy-tailed Random Variables in Prospective-loss Process

In this paper, we study the precise large deviations for the prospectiveloss process with consistently varying tails. The obtained results improve some related known ones.     

Large Deviations in Random Perturbations of Axiom a Basic Sets

The paper first proves a result on the upper semi-continuityof the entropy map of random bundle maps, and then proves somelarge deviation results for random maps perturbations of anAxiom A diffeomorphism.     

Quenched Free Energy and Large Deviations for Random Walks in Random Potentials

We study quenched distributions on random walks in a random potential on integer lattices of arbitrary dimension and with an arbitrary finite set of admissible steps. The potential can be unbounded and can depend on a few steps of the walk. Directed, undirected, and stretched polymers, as well as random walk in random environment, are covered. The restriction needed is on the moment of the potential, in relation to the degree of mixing of the ergodic environment. We derive two variational formulas for the limiting quenched free energy and prove a process‐level quenched large deviation principle (LDP) for the empirical measure. As a corollary we obtain LDPs for types of random walks in random environments not covered by earlier results. © 2012 Wiley Periodicals, Inc.     

A Hodograph Transformation Applicable to a Large Class of PDEs

We present a hodograph transformation providing solutions for a wide family of multidimensional nonlinear partial differential equations and discuss several applications to concrete examples.     

On Large Deviations of Multivariate Heavy-Tailed Random Walks

Let {S _n ;n=1,2,…} be a random walk in R ^d and E(S ₁)=(μ ₁,…,μ _d ). Let a _j >μ _j for j=1,…,d and A=(a ₁,∞)×⋅⋅⋅×(a _d ,∞). We are interested in the probability P(S _n /n∈A) for large n in the case where the components of S ₁ are heavy tailed. An objective is to associate an exact power with the aforementioned probability. We also derive sharper asymptotic bounds for the probability and show that in essence, the occurrence of the event {S _n /n∈A} is caused by large single increments of the components in a specific way.      

Large Deviations for a Class of Scale-Free Statistics under the Gamma Distribution

We study large deviations for sums of functions of -distributed random values normalized by their sample mean. In particular, large deviations are found for several well-known scale-free exponentiality tests such as the Moran and Liliefors tests. We also obtain some new results for a class of goodness-of-fit tests for the uniform distribution based on spacings. Bibliography: 16 titles.__________Translated from Zapiski Nauchnykh Seminarov POMI, Vol. 298, 2003, pp. 252–279.