R/S统计量的中偏差

在某种弱于标准大偏差条件下,甚至不需要二阶矩假设,给出了R/S统计量的中偏差原理.     

带小随机扰动的中偏差原理(英文)

本文研究了带小随机扰动的中偏差原理.运用收缩原理和指数逼近方法,Freidlin-Wentzell定理给出了Xε的大偏差原理,从而得到了Xε的中偏差原理.     

经验过程大偏差

本文研究了独立但不同分布的随机变量序列的经验过程大偏差原理.运用Talagrand-Ledoux偏差不等式建立了该经验过程大偏差估计的充分和必要条件.     

带移民超布朗运动的占位时中偏差

本文证明了当底空间维数d≥3时,一类带移民超布朗运动占位时过程的中偏差,其移民由Lebesgue 测度控制.可以清楚地看出,中偏差的规范化因子和速度函数恰好介于中心极限定理和大偏差之间,在 这个意义下,中偏差填补了中心极限定理和大偏差之间的空白.     

带移民超布朗运动的占位时中偏差

本文证明了当底空间维数d(≥)3时,一类带移民超布朗运动占位时过程的中偏差,其移民由Lebesgue测度控制.可以清楚地看出,中偏差的规范化因子和速度函数恰好介于中心极限定理和大偏差之间,在这个意义下,中偏差填补了中心极限定理和大偏差之间的空白.     

一类带泊松鞅测度Lévy区域的泛函重对数律

本文研究了一类带泊松鞅测度的Lévy区域的泛函重对数律,我们首先给出一类线性随机微分方程中偏差速率函数的广义逆表示,然后通过大偏差方法,我们给出了它们的泛函极限形式.     

一类带移民超Brown运动的中偏差

证明了一类带移民的超Brown运动在各种维数下的中偏差，从而填补了中心极限定理和大偏差之间的空白.     

分枝布朗运动最右位置的中偏差（英文）

我们研究了分枝布朗运动最右粒子位置的中偏差概率,并且得到了中偏差函数.首先,Chauvin和Rouault考虑了分枝布朗运动最右位置的大偏差概率.最近,Derrida和Shi对同样的模型研究了其下偏差.相比之下,我们的结果更加广泛.     

GL-统计量的中偏差及大偏差

本文讨论GL-统计量的中偏差。Cramer型大偏差及 Chernoff型大偏差。其中关于GL-统计量的中偏差及Cramer 型大偏差结果推广了Vandemaele et al有关L-统计量,U-统计量的结果。这里,首次给出关于 U-统计量的 Chernoff型大偏差。应用它得到 GL-统计量的 Chernoff型的大偏差。所采用的方法为 Gateux微分逼近和 Bahadur 分位数表示法。     

Brown运动连续模在Holder范数下的收敛速率

应用Brown运动在Holder范数下的大偏差和小偏差得到了Brown运动连续模在Holder范数下的泛函极限的收敛速率.     

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.     

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.     

Large and moderate deviations principles for kernel estimators of the multivariate regression

In this paper, we prove large deviations principle for the Nadaraya-Watson estimator and for the semi-recursive kernel estimator of the regression in the multidimensional case. Under suitable conditions, we show that the rate function is a good rate function. We thus generalize the results already obtained in the one-dimensional case for the Nadaraya-Watson estimator. Moreover, we give a moderate deviations principle for these two estimators. It turns out that the rate function obtained in the moderate deviations principle for the semi-recursive estimator is larger than the one obtained for the Nadaraya-Watson estimator.      

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.     

Asymptotic behaviors for functionals of random dynamical systems

In this article, we consider asymptotic behaviors for functionals of dynamical systems with small random perturbations. First, we present a deviation inequality for Gaussian approximation of dynamical systems with small random perturbations under Hölder norms and establish the moderate deviation principle and the central limit theorem for the dynamical systems by the deviation inequality. Then, applying these results to forward-backward stochastic differential equations and diffusions in small time intervals, combining the delta method in large deviations, we give a moderate deviation principle for solutions of forward-backward stochastic differential equations with small random perturbations, and obtain the central limit theorem, the moderate deviation principle and the iterated logarithm law for functionals of diffusions in small time intervals.     

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.     

Large and moderate deviations and exponential convergence for stochastic damping Hamiltonian systems

A classical damping Hamiltonian system perturbed by a random force is considered. The locally uniform large deviation principle of Donsker and Varadhan is established for its occupation empirical measures for large time, under the condition, roughly speaking, that the force driven by the potential grows infinitely at infinity. Under the weaker condition that this force remains greater than some positive constant at infinity, we show that the system converges to its equilibrium measure with exponential rate, and obeys moreover the moderate deviation principle. Those results are obtained by constructing appropriate Lyapunov test functions, and are based on some results about large and moderate deviations and exponential convergence for general strong-Feller Markov processes. Moreover, these conditions on the potential are shown to be sharp.     

MODERATE DEVIATIONS FROM HYDRODYNAMIC LIMIT OF A GINZBURG-LANDAU MODEL

The authors consider the moderate deviations of hydrodynamic limit for Ginzburg-Landau models. The moderate deviation principle of hydrodynamic limit for a specific Ginzburg-Landau model is obtained and an explicit formula of the rate function is derived.     

Large deviations for Gibbs random fields

A large deviation principle for Gibbs random fields on Z^d is proven and a corresponding large deviations proof of the Gibbs variational formula is given. A generalization of the Lanford theory of large deviations is also obtained.This work was partially supported by NSF-DMR81-14726     

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.