Moderate deviations and functional limits for random processes with stationary and independent increments

We first give a functional moderate deviation principle for random processes with stationary and independent increments under the Ledoux's condition. Then we apply the result to the functional limits for increments of the processes and obtain some Csorgo-Revesz type functional laws of the iterated logarithm.     

Limit theorems for inverse process <Emphasis Type="Italic">T</Emphasis><Subscript><Emphasis Type="Italic">n</Emphasis></Subscript> of Hawkes process

We consider linear Hawkes process N _t and its inverse process T _n . The limit theorems for N _t are well known and studied by many authors. In this paper, we study the limit theorems for T _n . In particular, we investigate the law of large numbers, the central limit theorem and the large deviation principle for T _n . The main tool of the proof is based on immigration-birth representation and the observations on the relation between N _t and T _n .     

Markov过程的一致中偏差──离散参数情形

本文证明：离散参数Ｍａｒｋｏｖ过程的一致中偏差原理成立的充要条件是Ｄｏｅｂｌｉｎ常返性（即：满足Ｄｏｅｂｌｉｎ条件且是Ｈａｒｒｉｓ常返的）。     

Moderate deviations from the hydrodynamic limit of the symmetric exclusion process

We investigate the moderate deviations from the hydrodynamic limit of the empirical density ofparticles and obtain a moderate deviation principle for a symmetric exclusion process.     

Large deviations for conditional Volterra processes

In this article we investigate a problem of large deviations for continuous Gaussian Volterra processes, conditioned to follow a fixed trajectory up to a fixed time T > 0, in order to establish the behavior of the process in the near future after T and to give an asymptotic estimate of the exit probability of its bridge. Some examples are considered.     

次线性期望下独立随机变量列的大偏差和中偏差

本文研究在次线性期望下的独立随机变量列的大偏差和中偏差原理. 利用次可加方法, 我们得 到次线性期望下的大偏差原理. 与次线性期望下的中心极限定理相应的中偏差原理也被建立.     

Large deviations ordering of point processes in some queueing networks

Given a stochastic ordering between point processes, say that a p.p. N is smooth if it is less than the Poisson process with the same average intensity for this ordering. In this article we investigate whether initially smooth processes retain their smoothness as they cross a network of FIFO ·/D/1 queues along fixed routes. For the so-called strong variability ordering we show that point processes remain smooth as they proceed through a tandem of quasi-saturated (i.e., loaded to 1) M+·/D/1 queues. We then introduce the Large Deviations ordering, which involves comparison of the rate functions associated with Large Deviations Principles satisfied by the point processes. For this ordering, we show that smoothness is retained when the processes cross a feed-forward network of unsaturated ·/D/1 queues. We also examine the LD characteristics of a deterministic p.p. at the output of an M+·/D/1 queue. This revised version was published online in June 2006 with corrections to the Cover Date.     

Large deviations of renewal processes

Limit theorems for large deviations of renewal processes are presented. One result is for a terminating renewal process with small probability of terminating. These theorems are analogous to the classical Cramer and Feller large deviation theorems for sums of independent random variables.     

Moderate deviations and central limit theorem for small perturbation Wishart processes

Let X^ε be a small perturbation Wishart process with values in the set of positive definite matrices of size m, i.e., the process X^ε is the solution of stochastic differential equation with non-Lipschitz diffusion coefficient： dXt^ε = √εXt^εtdBt＇ ＋ dBt＇√εXt^ε ＋ ρImdt, X0 = x, where B is an rn x m matrix valued Brownian motion and B＇ denotes the transpose of the matrix B. In this paper, we prove that { （Xt^ε-Xt^0）/√εh^2（ε）,ε 〉 0} satisfies a large deviation principle, and （Xt^ε - Xt^0）/√ε converges to a Gaussian process, where h（ε） → ＋∞ and √ε h（ε） →0 as ε →0. A moderate deviation principle and a functional central limit theorem for the eigenvalue process of X^ε are also obtained by the delta method.     

Moderate deviations for the quenched mean of the super-Brownian motion with random immigration

Moderate deviations for the quenched mean of the super-Brownian motion with random immigration are proved for 3≤d≤6, which fills in the gap between central limit theorem(CLT)and large deviation principle(LDP).     

Large deviations for perturbed reflected diffusion processes

In this article, we establish a large deviation principle for the solutions of perturbed reflected diffusion processes. The key is to prove a uniform Freidlin–Ventzell estimate of perturbed diffusion processes.     

Large deviations and moderate deviations for kernel density estimators of directional data

Let fn be the non-parametric kernel density estimator of directional data based on a kernel function K and a sequence of independent and identically distributed random variables taking values in d-dimensional unit sphere Sd-1. It is proved that if the kernel function is a function with bounded variation and the density function f of the random variables is continuous, then large deviation principle and moderate deviation principle for {sup x∈sd-1 ｜fn（x） - E（fn（x））｜, n ≥ 1} hold.     

三维Wiener Sausage体积的中偏差

令{β(s),s≥0}表示R~3空间中的标准Brown运动,|W_r(t)|表示由{β(s),s≥0}产生的观察至时间t且以r为半径的Wiener sausage的体积.由中心极限定理可知,(|W_r(t)|-E|W_r(t)|)/(?)弱收敛至正态分布.本文研究这种情况下的中偏差.     

Moderate deviations for estimators under exponentially stochastic differentiability conditions

We introduce two exponentially stochastic differentiability conditions to study moderate deviations for M-estimators. Under a generalized exponentially stochastic differentiability condition, a moderate deviation principle is established. Some sufficient conditions of the exponentially stochastic differentiability and examples are also given.     

Moderate Deviations and Large Deviations for Kernel Density Estimators

Let f _n be the non-parametric kernel density estimator based on a kernel function K and a sequence of independent and identically distributed random variables taking values in ^d . It is proved that if the kernel function is an integrable function with bounded variation, and the common density function f of the random variables is continuous and f(x) 0 as |x| , then the moderate deviation principle and large deviation principle for hold.     

Moderate deviations for randomly perturbed dynamical systems

A Moderate Deviation Principle is established for random processes arising as small random perturbations of one-dimensional dynamical systems of the form X_n=f(X_n−1). Unlike in the Large Deviations Theory the resulting rate function is independent of the underlying noise distribution, and is always quadratic. This allows one to obtain explicit formulae for the asymptotics of probabilities of the process staying in a small tube around the deterministic system. Using these, explicit formulae for the asymptotics of exit times are obtained. Results are specified for the case when the dynamical system is periodic, and imply stability of such systems. Finally, results are applied to the model of density-dependent branching processes.     

LARGE DEVIATIONS AND MODERATE DEVIATIONS FOR m-NEGATIVELY ASSOCIATED RANDOM VARIABLES

M-negatively associated random variables,which generalizes the classical one of negatively associated random variables and includes m-dependent sequences as its par- ticular case,are introduced and studied.Large deviation principles and moderate devi- ation upper bounds for stationary m-negatively associated random variables are proved. Kolmogorov-type and Marcinkiewicz-type strong laws of large numbers as well as the three series theorem for m-negatively associated random variables are also given.     

Moderate deviations in subsampling distribution estimation

In Politis and Romano (1994) the subsampling methodology was put forth for approximating the sampling distribution (and the corresponding quantiles) of general statistics from i.i.d. and stationary data. In this note, we address the question of how well the subsampling distribution approximates the tail of the target distribution. In the regular setting of the sample mean of an -dependent sequence we show a moderate deviation property of the subsampling distribution.