具有均匀结构的多元$t$\,-模型的局部影响分析

本文研究具有均匀结构的多元$t$\,-模型的局部影响分析问题\bd 依据Cook的曲率度量, 我们考虑了微小扰动对统计推断的影响, 由此导出了局部影响分析中最为关心的统计量---最大曲率方向\bd作为一种应用, 本文还祥细讨论了常见的协方差加权扰动形式.     

多元正态模型的局部影响评价

本文就多元分析中，对具有多元正态分布的多元数据，给出了它们在观测加权（ｃａｓｅ－Ｗｅｉｇｈｔｓ）扰动下模型的局部影响评价，通过实例分析，证实了所得结论的有效性。     

单纯形分布非线性模型的局部影响分析及其应用

讨论了单纯形分布非线性模型的局部影响分析问题.应用Cook(1986)的影响曲率方法研究了该模型关于微小扰动的局部影响,得到了局部影响分析的曲率度量.同时也应用PoonW Y和Poon Y S(1997)的保形法曲率方法研究了该模型的局部影响.对常见的扰动模型,分别进行了局部影响分析,得到了计算影响矩阵的简洁公式.最后还研究了两个实例,说明文中方法的应用价值.     

具有Rao简单结构的多元T-模型的局部影响分析

本文应用以Kullback-Leibler散度为基础的Bayesian局部影响方法，对具有Rao简单结构的多元T-模型进行了局部影响分析．在确定了先验分布假设下，详细地研究了这个模型的Bayesian Hessian矩阵，作为应用，特别考虑了常见的加权协方差扰动形式．     

混合线性模型效应参数的Bayes局部影响分析

该文研究混合线性模型效应参数的Ｂａｙｅｓ局部影响评价问题．导出了混合线性模型在各种扰动下效应参数的Ｂａｙｅｓ局部影响度量，并给出了平衡单向分类随机效应模型下的一些结果．最后通过实例分析，以证实该文方法的有效性．     

线性模型和线性EV模型中的T-型回归估计和EM算法

本文对于线性函数关系EV模型定义了$t$\,-型回归估计, 并对于普通线性模型和线性函数关系EV模型给出了计算$t$\,-型回归估计的EM算法, 同时获得了估计的相合性\bd 模拟结果表明由EM算法获得的$t$\,-型回归估计的表现良好.     

多元线性回归置信域的局部影响

运用Ｃｏｏｋ（１９８６）的局部影响法评价多元线性回归模型的微小扰动对回归系数置信域的影响，扰动方式包括协方差阵扰动，自变量扰动和因变量扰动．     

一般形式下的协方差分析模型的局部影响分析

利用局部影响的方法对一般形式下的协方差分析模型进行了讨论.把数据点或数据子集的扰动拓展到更广泛的扰动模式并进行了局部影响评价,导出了一般形式下的协方差分析模型在方差扰动下局部影响的曲率度量.     

数据变换模型的局部影响分析

数据变换模型的局部影响分析韦博成，史建清（东南大学数学系，南京２１００１８）ＬＯＣＡＬＩＮＦＬＵＥＮＣＥＡＮＡＬＹＳＩＳＦＯＲＲＥＧＲＥＳＳＩＯＮＴＲＡＮＳＦＯＲＭＡＴＩＯＮＭＯＤＥＬＳ￥ＷＥＩＢＯＣＨＥＮＧＡＮＤＳＨＩＪＩＡＮＧＩＮＧ（Ｄｅｐｅｒｔ...     

广义多元Beta分布

本文把广义Beta分布(Eugene(2001))推广到了多元的情形, 研究了多元Beta分布的矩母函数,以及广义多元Beta分布的边际分布、条件分布及回归函数.给出了他们在次序统计量中的应用.     

���Volterra���̵����ļ��޶���

??In this paper, we study a class of stochastic Volterra equations, which include the stochastic differential equation driven by fractional Brownian motion. By using a maximal inequality due to It\^o (1979), we establish the central limit theorem for stochastic Volterra equation on the continuous path space, with respect to the uniform norm.     

Large Deviations from the Almost Everywhere Central Limit Theorem

We prove large deviation principles for the almost everywhere central limit theorem, assuming that the i.i.d. summands have finite moments of all orders. The level 3 rate function is a specific entropy relative to Wiener measure and the level 2 rate the Donsker-Varadhan entropy of the Ornstein-Uhlenbeck process. In particular, the rate functions are independent of the particular distribution of the i.i.d. process under study. We deduce these results from a large deviation theory for Brownian motion via Skorokhod's representation of random walk as Brownian motion evaluated at random times. The results for Brownian motion come from the well-known large deviation theory of the Ornstein-Uhlenbeck process, by a mapping between the two processes.     

Estimating the Hurst Parameter

Let be a fractional Brownian motion with parameter 0 < H < 1. We are interested in the estimation of this parameter. To achieve this goal, we consider certain functionals of the second order increments of b _H (·), using variation technics. Based on an almost-sure convergence theorem for general functionals, we single out particular functionals that allows to construct certain regression models for the parameter H. We show that this regression based estimator for H is asymptotically unbiased, consistent and that it satisfies a Central Limit Theorem.      

随机Volterra积分方程的广义样本解

本文研究了随机积分方程的广义样本解.利用随机微分方程转换为带参数常微分方程的方法,给出了一类随机Volterra积分方程的广义样本解,这类方程在许多应用领域是常见的.     

Regularity Properties of Some Stochastic Volterra Integrals with Singular Kernel

We prove the Hölder continuity of some stochastic Volterra integrals, with singular kernels, under integrability assumptions on the integrand. Some applications to processes arising in the analysis of the fractional Brownian motion are given. The main tool is the embedding of some Besov spaces into some sets of Hölder continuous functions.     

Kolmogorov equation and large-time behaviour for fractional brownian motion driven linear sde’s

We consider a stochastic process X _t ^x which solves an equation

Density estimates and central limit theorem for the functional of fractional SDEs

In this paper, we consider a general class of functionals of stochastic differential equations driven by fractional Brownian motion. For this class, we obtain Gaussian estimates for the density and a quantitative central limit theorem. The main tools of the paper are the techniques of Malliavin calculus.     

The Increment Ratio statistic under deterministic trends

The Increment Ratio (IR) statistic (see (1.1) below) was introduced in Surgailis et al. [16]. The IR statistic can be used for testing nonparametric hypotheses for d-integrated (−1/2 < d < 5/4) behavior of time series, including short memory (d = 0), (stationary) long-memory (0 < d < 1/2), and unit roots (d = 1). For stationary/stationary increment Gaussian observations, in [16], a rate of decay of the bias of the IR statistic and a central limit theorem are obtained. In this paper, we study the asymptotic distribution of the IR statistic under the model X _t = X _t⁰ + g _N(t) (t = 1, …, N), where X _t⁰ is a stationary/stationary increment Gaussian process as in [16], and g _N(t) is a slowly varying deterministic trend. In particular, we obtain sufficient conditions on X _t⁰ and g _N(t) under which the IR test has the same asymptotic confidence intervals as in the absence of the trend. We also discuss the asymptotic distribution of the IR statistic under change-points in mean and scale parameters. Partially supported by the bilateral France-Lithuania scientific project Gilibert and Lithuanian State Science and Studies Foundation, grant No. T-25/08.     

Functional Limit Theorems for Multiparameter Fractional Brownian Motion

We prove a general functional limit theorem for multiparameter fractional Brownian motion. The functional law of the iterated logarithm, functional Lévy’s modulus of continuity and many other results are its particular cases. Applications to approximation theory are discussed.      

A central limit theorem for branching Brownian motion with random immigration

We establish a central limit theorem for a branching Brownian motion with random immigration under the annealed law,where the immigration is determined by another branching Brownian motion.The limit is a Gaussian random measure and the normalization is t3/4for d=3 and t1/2for d≥4,where in the critical dimension d=4 both the immigration and the branching Brownian motion itself make contributions to the covariance of the limit.