E—值独立随机变量部分和大偏差及应用

本文给出了Ｅ－值随机变量之部分和大偏差定理（Ｅ是一类局部凸空间）。作为其应用，解决了独立弱收敛随机变量列的经验分布的大偏差问题，从而推广了Ｄｏｎｓｋｅｒ－Ｖａｒａｄｈａｎ的结果。     

-混合随机变量的泛函型Erds-Rényi大数定律

本文证明了■－混合随机变量的Ｅｒｄｏｓ－Ｒｅｎｙｉ大数定律特别地，以概率１，Ｗ(Ｎ，ｎ)＝的极限点集是相对紧的并与函数空间的大偏差速率函数相联系，其中 是取值于 Ｂａｎａｃｈ空间的■－混合随机变量， ｈ（Ｎ）＝［ｃｌｏｇＮ］．     

NA及B-值随机变量序列的平均移动过程的大偏差原理

本文在比较一般的条件下建立了两个大偏差原理：平稳NA随机变量序列的平均移动过程的大偏差原理和独立同分布的B-值随机变量序列的平均移动过程的大偏差原理。     

上尾渐近独立随机变量和的大偏差的渐近下界(英文)

本文研究了多元风险模型中服从长尾分布的带上尾渐近独立的随机变量和的大偏差渐近下界.利用大偏差的经典求法,得到了随机变量的非随机和和随机和的大偏差表达式,推广了独立同分布情形下的相关结论.     

L族END的多元部分和的精确大偏差下界

从保险的实际出发,研究服从长尾分布族(L族)上的多元风险模型中随机变量序列的部分和的精确大偏差,其中假设随机变量序列是一列延拓负相依(END)的、同分布的随机变量序列,利用基于求L族的精确大偏差的方法得到了随机变量部分和的渐近下界.     

负相依随机变量之和的概率大偏差不等式

本文建立了负相依随机变量序列的概率大偏差不等式，并推广了以往文献的结果．     

多元模型的长尾相依大偏差的下界

研究了在多元模型中的服从长尾分布且带有负相依的随机变量和的尾概率,在给定的一些条件下通过采用多元大偏差的方法得到了随机变量的非随机和和随机和的大偏差的下界,推广了相应的独立同分布情形下的结论.     

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.     

ND序列部分和的大偏差和强收敛性

利用ND随机变量序列的矩不等式、极大值不等式以及随机变量的截尾方法,重点研究了ND随机变量序列部分和的大偏差结果和强收敛性,推广了文献中一些相依随机变量序列的若干相应结果.     

宽上限相依的随机变量和的精确大偏差

通过研究了长尾上的带宽上限相依的随机变量和的精确大偏差,利用经典大偏差的方法,得到了非随机和和随机和的两种渐近结果.     

LARGE DEVIATION FOR EMPIRICAL FIELD OF A SYMMETRIC MEASURE

This paper discusses the large deviation for the empirical field of a symmetricmeasure.The lower bound of the large deviation is obtained by extending the classcal Shannon-McMillan theorem. The upper bound is established by means of Legendre transformation and the minimax theorem.     

A strong large deviation theorem

We prove a strong large deviation theorem for an arbitrary sequence of random variables, that is, we establish a full asymptotic expansion of large deviation type for the tail probabilities. An Edgeworth expansion is required to derive the result. We illustrate our theorem with two statistical applications: the sample variance and the kernel density estimator.     

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.     

Moderate deviation for super-Brownian motion with immigration

We prove a moderate deviation principle for a super-Brownian motion with im-migration of all dimensions, and consequently fill the gap between the central limit theoremand large deviation principle.     

Large deviations of realized volatility

This paper studies large and moderate deviation properties of a realized volatility statistic of high frequency financial data. We establish a large deviation principle for the realized volatility when the number of high frequency observations in a fixed time interval increases to infinity. Our large deviation result can be used to evaluate tail probabilities of the realized volatility. We also derive a moderate deviation rate function for a standardized realized volatility statistic. The moderate deviation result is useful for assessing the validity of normal approximations based on the central limit theorem. In particular, it clarifies that there exists a trade-off between the accuracy of the normal approximations and the path regularity of an underlying volatility process. Our large and moderate deviation results complement the existing asymptotic theory on high frequency data. In addition, the paper contributes to the literature of large deviation theory in that the theory is extended to a high frequency data environment.     

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.     

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.     

Large deviations under a viewpoint of metric geometry: Measure-valued process cases

We illustrate a metric geometry viewpoint for large deviation principles by analyzing the proof of a long-standing conjecture on an explicit Schilder-type theorem for super-Brownian motions given by the authors recently, and by understanding sample path large deviations for Fleming-Viot processes.     

公理A自同态的统计性结果（英文）

本文对公理A自同态建立了中心极限定理和大偏差估计，并且复习了已知的有关统计性结果。     

The expansion theorem for the deviation integra

The so-called deviation integral (functional) describes the logarithmic asymptotics of the probabilities of large deviations for random walks generated by sums of random variables or vectors. Here an important role is played by the expansion theorem for the deviation integral in which, for an arbitrary function of bounded variation, the deviation integral is represented as the sum of suitable integrals of the absolutely continuous, singular, and discrete components composing this function. The expansion theorem for the deviation integral was proved by A. A. Borovkov and the author in [9] under some simplifying assumptions. In this article, we waive these assumptions and prove the expansion theorem in the general form.