相依条件下滑动平均过程精确渐近性的一般规律（英文）

本文研究了相依条件下滑动平均过程完全收敛的精确渐近性问题.利用正态分布逼近的方法及相关不等式,获得了精确渐近性的一般规律,推广了对数率和重对数率精确渐近性的已有结果.     

弱相依半参数回归模型的加权小波估计的渐近正态性

本文研究了半参数回归模型y_i=X'_iβ+g(t_i)+e_i,i=1,2,···,n,其中{e_i}为ψ-弱相依随机误差序列.利用小波估计的方法得到了参数、非参数的加权小波估计量.在相当一般的条件下,获得了这些小波估计量的渐近正态性,不仅推广了半参数回归模型的相应结果,而且在一定程度上统一了相依半参数回归模型的渐近正态性的理论.     

线性模型误差分布估计的渐近理论

考虑线性模型,其误差是i.i.d具有公共的未知密度f(x).基于残差构造f(x)的非参数估计f_n(x),本文在作者以往工作的基础上进一步建立了f_n(x)的L₁-模相合性、渐近正态性及重对数律,而所施加的条件则是十分一般的,这些结果完善了误差分布的渐近理论.     

在最少条件下的对数律精确渐近性

设$X_1,X_2,\cdots$为独立同分布随机变量, 记S_n=X_1+\cdots+X_n,\;M_n=\max\limits_{k\le n}|S_k|,\;n\ge1$. 本文在充分必要条件下给出了$M_n$和$S_n$的对数律之精确渐近性.     

NA序列的重对数矩收敛的精确渐近性

假设{X_n,n≥1}为一列严平稳的NA随机变量,期望为零,方差有限.设S_n=∑_(i=1)~n∑X_i,M_n=max_(1≤i≤n)|S_i|.在适当的条件下,得到了一类NA序列部分和部分和最大值重对数矩收敛的精确渐近性.     

渐近半伪压缩映射的强收敛的充分必要条件

本文研究了渐近半伪压缩映射.应用带误差项的修改的Ishikawa迭代序列,得到了一致L-Lipschitzian渐近半伪压缩映射逼近其不动点的强收敛的充分必要条件.     

非线性半参数回归模型中参数的经验似然置信域

该文考虑非线性半参数回归模型,构造了模型中未知参数的经验对数似然比统计量,证明了所提出的统计量具有渐近Χ²分布,由此结果可以用来构造未知参数的置信域.另外,该文也构造了未知参数 的最小二乘估计量,并证明了它的渐近性质.仅就置信域精度及其覆盖概率大小方面,通过模拟研究比较了经验似然方法与最小二乘法的优劣.     

左截断右删失数据下半参数模型风险率函数估计

文章给出了右删失左截断数据半参数模型下的风险率函数估计,讨论了风险率函数估计的渐近性质,获得了这些估计的渐近正态性,对数律和重对数律.由于假定删失机制服从半参数模型下,从而知道模型的更多信息,因此对于给出参数的极大似然估计,可以改进风险率函数估计的渐近性质.也就是说,删失数据模型具有半参数的辅助信息下, 风险率函数估计的渐近方差比通常的完全非参数的估计的渐近方差更小.这说明加入了额外的信息提高了风险率函数估计的效率.     

高频数据连续部分杠杆效应的估计

对股票价格与其波动率之间的负相关性的发现,引发了对高频金融数据杠杆效应的研究热潮.对于高频数据连续时间条件下满足伊藤半鞅模型的对数价格过程和波动率过程,定义了连续部分杠杆效应(CLE),并用临近窗口和向下截断方法,采用二次变差来构造相应的估计量,进一步研究了该估计量的相合性和渐近正态性,最后给出了定理证明.     

一般状态空间跳过程的不可约性

本文研究了一般状态空间跳过程的ψ-不可约性.利用与马氏链类似的方法,得到了最大不可约测度及其性质,推广了离散时间一般状态空间马氏链的结果,作为应用,给出了C∈E+的充分条件.最后,证明了跳过程、骨架链、跳跃链和预解链的ψ-不可约是等价的.     

The precise asymptotic behavior of parameter estimators in Ornstein-Uhlenbeck process

In the present paper, we study the asymptotic behavior for estimator of the drift parameter in an Ornstein-Uhlenbeck process. The Lr-convergence rate and the precise asymptotics in the law of iterated logarithm and in the law of logarithm for the estimator are obtained. Moreover, we also get the complete moment convergence of this estimator. The main method of this paper is the deviation inequality for the quadratic functional.     

NA序列重对数律的几个极限定理

设{X_n;n≥1}均值为零、方差有限的NA平稳序列。记S_n=∑_(k=1)~n X_k,M_n=maxk≤n|S_k|,n≥1.假设σ~2=EX_1~2+2∑_(k=2)~∞EX_1X_k>0。本文讨论了:当ε 0时,P{M_n≥εσ(2nloglogn)~(1/2)的一类加权级数的精确渐近性质,以及当ε∞时,P{M_n≤εσ(π~2n/(8loglogn))~(1/2)}的一类加权级数的精确渐近性质。这些性质与重对数律和Chung重对数律的速度有关。     

Precise asymptotics for random matrices and random growth models

The author considers the largest eigenvaiues of random matrices from Gaussian unitary ensemble and Laguerre unitary ensemble, and the rightmost charge in certain random growth models. We obtain some precise asymptotics results, which are in a sense similar to the precise asymptotics for sums of independent random variables in the context of the law of large numbers and complete convergence. Our proofs depend heavily upon the upper and lower tail estimates for random matrices and random growth models. The Tracy-Widom distribution plays a central role as well.     

A Functional Law of the Iterated Logarithm for U-Statistic Type Processes

We prove a functional law of the iterated logarithm for U-statistics type processes. The result is used to determine the almost sure set of limit points for change-point estimators.     

Rates of Convergence and Law of the Iterated Logarithm for U-Processes

We study in this paper some limit theorems for U-processes. We calculate rates of convergence in the central limit theorem of nondegenerate U-processes under metric entropy with bracketing condition. In application, we improve upon the law of the iterated logarithm of Arcones. All calculations use the Ossiander chaining procedure.     

A two-step sequential procedure for detecting an epidemic change

The typical approach in change-point theory is to perform the statistical analysis based on a sample of fixed size. Alternatively, and this is our approach, one observes some random phenomenon sequentially and takes action as soon as one observes some statistically significant deviation from the “normal” behaviour. In this paper we focus on epidemic changes, that is, a first change (the outbreak) when there is a change in the distribution, and a second change, when the process regains its ordinary structure. Based on the counting process related to the original process observed at equidistant time points, we propose some stopping rules for this to happen and consider their asymptotics under the null hypothesis as well as under alternatives. The main basis for the proofs are strong invariance principles for renewal processes, extreme value asymptotics for Gaussian processes, and the law of the iterated logarithm.     

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设{X,Xn;n≥1}为吸引域为正态吸引域的独立同分布的随机变量序列,本文证明了其自正则部分和的完全矩的精确渐近性.     

SOME LIMIT PROPERTIES OF LOCAL TIME FOR RANDOM WALK

Let X,X1,X2 be i. i. d. random variables with EX^2＋δ〈∞ （for some δ〉0）. Consider a one dimensional random walk S={Sn}n≥0, starting from S0 =0. Let ζ＊ （n）=supx∈zζ（x,n）,ζ（x,n） =#{0≤k≤n：[Sk]=x}. A strong approximation of ζ（n） by the local time for Wiener process is presented and the limsup type and liminf-type laws of iterated logarithm of the maximum local time ζ＊（n） are obtained. Furthermore,the precise asymptoties in the law of iterated logarithm of ζ＊（n） is proved.     

Convergence of a stochastic particle approximation for fractional scalar conservation laws

We are interested in a probabilistic approximation of the solution to scalar conservation laws with fractional diffusion and nonlinear drift. The probabilistic interpretation of this equation is based on a stochastic differential equation driven by an α-stable Lévy process and involving a nonlinear drift. The approximation is constructed using a system of particles following a time-discretized version of this stochastic differential equation, with nonlinearity replaced by interaction. We prove convergence of the particle approximation to the solution of the conservation law as the number of particles tends to infinity whereas the discretization step tends to 0 in some precise asymptotics.