无界函数指标集上经验过程大偏差的局部概率不等式及应用

提出并研究了无界函数指标集合上经验过程的大偏差尾部的局部概率指数不等式, 给出了一种新的截割原始概率空间的方法和新的对称化方法. 利用这些方法, 导出了无界函数指标集合上非i.i.d.独立样本的经验过程大偏差尾部的局部概率指数不等式, 并给出了它们的若干应用. 作为应用的一个附带结果, 在Kolmogorov定理所给的条件下, 将Kolmogorov关于非i.i.d的独立随机变量和的强收敛结果推广到无界函数指标集上的经验过程情形, 并且得到了无界函数指标集上经验过程大偏差尾部的局部概率指数不等式和对数律.     

关于基于Delta-序列的密度估计的大偏差的一个注记

本文研究了基于一个delta-序列和一列独立同分布且取值于 ??綆 随机变量的非参数密度估计的对称检验问题,用经验过程的方法,得到了相应的量满足大偏差原理,推广了文献[3]的结果.     

独立不同分布经验过程的大偏差原理

设（Xn) n≥1是取值于可测空间（E,B^A)的一串独立随机变量,考虑经验过程Ln(f)=1/n ∑i=1^nf(Xi),f属于某个有界函数集F。运用Talagrand-Ledoux偏差不等式,我们得到其大偏差估计的充分必要条件。最后推广到无界函数族情形。     

含估计参数的加权经验过程

在讨论(广义)非参数似然比拟合优度检验时,加权经验过程理论是一个非常重要的基础.但对含估计参数的加权经验过程理论,目前文献中很少讨论.对含估计参数的加权经验过程的上界型和积分型两种统计量的近似分布进行了讨论,给出了较一般的结果.所得结论叮以为进一步讨论复合零假设下(广义)非参似然比拟合优度检验提供理论基础.     

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.     

分数OU模型参数估计的大偏差

本文考虑了分数OU模型参数估计的大偏差,通过Laplace变换的技巧,得到了极大似然估计的大偏差.     

无界函数指标集合上平稳的β-混合序列的经验过程的收敛速度

对无界的实函数指标集的情形进行了讨论,提出了一种新的截断方法,用以处理无界的函数指标集上严平稳β-混合随机序列的经验过程,并得到了该经验过程的一致收敛速度.     

部分转移风险过程的大偏差

本文研究了部分转移风险过程的大偏差问题.利用构造指数鞅的方法,得到了部分转移风险过程的大偏差.该结果给出部分转移风险过程的一个渐近行为.     

连续型经验过程及其Bootstrap逼近

本文利用样条函数将经验分布函数光滑化,从而得到了连续型经验分布函数,进而引进了连续型经验过程,本文讨论了连续型经验分布函数和连续型经验过程的渐进分布,并且证明了连续型经验过程的Bootstrap逼近成立。     

矩阵值Ornstein-Uhlenbeck过程的大偏差上界

本文研究了矩阵值Ornstein-Uhlenbeck过程的大偏差问题.通过构造指数鞅,得到了矩阵值Ornstein-Uhlenbeck过程的经验谱过程的大偏差上界,推广了厄米特布朗运动相应的结果.     

无穷维自回归过程的中偏差原理

本文考虑无穷维自回归过程经验协方差函数的中偏差原理,仅对自回归过程的随机扰动项做了高斯可积性的假设,这个条件比[4]中的对数Sobolev不等式要弱很多.主要利用了m-相依随机变量的中偏差结果和Ellis-Grtner定理,推广了[6]的结果.     

Large deviations for Glauber dynamics of continuous gas

This paper is devoted to the large deviation principles of the Glauber-type dynamics of finite or infinite volume continuous particle systems.We prove that the level-2 empirical process satisfies the large deviation principles in the weak convergence topology,while it does not satisfy the large deviation principles in the T-topology.     

LARGE DEVIATIONS FOR INFINITE DIMENSIONAL AND REVERSIBLE REACTION－DIFFUSION PROCESSES

ＬＡＲＧＥＤＥＶＩＡＴＩＯＮＳＦＯＲＩＮＦＩＮＩＴＥＤＩＭＥＮＳＩＯＮＡＬＡＮＤＲＥＶＥＲＳＩＢＬＥＲＥＡＣＴＩＯＮ－ＤＩＦＦＵＳＩＯＮＰＲＯＣＥＳＳＥＳＣＨＥＮＪＩＮＷＥＮ（陈金文）（ＤｅｐｅｒｔｍｅｎｔｏｆＡｐｐｌｉｅｄＭａｔｈｅｍａｔｉｃｓ，Ｔ...     

Moderate deviation principle for autoregressive processes

A moderate deviation principle for autoregressive processes is established. As statistical applications we provide the moderate deviation estimates of the least square and the Yule–Walker estimators of the parameter of an autoregressive process. The main assumption on the autoregressive process is the Gaussian integrability condition for the noise, which is weaker than the assumption of Logarithmic Sobolev Inequality in [H. Djellout, A. Guillin, L. Wu, Moderate deviations of empirical periodogram and nonlinear functionals of moving average processes, Ann. I. H. Poincaré-PR 42 (2006) 393–416].     

A large deviation principle form-variate von Mises-statistics and U-statistics

A large deviation principle form-variate von Mises-statistics and U-statistics with a kernel function satisfying natural moment conditions is proved. Sanov's large deviation result for the empirical distribution function and two fundamental conservation principles in large deviation theory are the main tools. The rate functions are “drawback”-entropy functionals.     

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.     

A local probability exponential inequality for the large deviation of an empirical process indexed by an unbounded class of functions and its application

A local probability exponential inequality for the tail of large deviation of an empirical process over an unbounded class of functions is proposed and studied. A new method of truncating the original probability space and a new symmetrization method are given. Using these methods, the local probability exponential inequalities for the tails of large deviations of empirical processes with non-i.i.d. independent samples over unbounded class of functions are established. Some applications of the inequalities are discussed. As an additional result of this paper, under the conditions of Kolmogorov theorem, the strong convergence results of Kolmogorov on sums of non-i.i.d. independent random variables are extended to the cases of empirical processes indexed by unbounded classes of functions, the local probability exponential inequalities and the laws of the logarithm for the empirical processes are obtained.     

LARGE DEVIATION FOR THE EMPIRICAL CORRELATION COEFFICIENT OF TWO GAUSSIAN RANDOM VARIABLES

In this article,the author obtains the large deviation principles for the empir- ical correlation coefficient of two Gaussian random variables X and Y.Especially,when considering two independent Gaussian random variables X,Y with the means EX. EY (both known),wherein the author gives two kinds of different proofs and gets the same results.     

Large deviations for empirical measures of switching diffusion processes with small parameters

We consider the asymptotic property of the diffusion processes with Markovian switching. For a general case, we prove a large deviation principle for empirical measures of switching diffusion processes with small parameters.