功率和的强逼近

设$\{\xi_n, n\geq 1\}$是正的随机变量序列, $\ep \xi_1=\theta>0$, 设$S_n = \sum\limits_{i=1}^n \xi_i, Y_n=n\theta\log (S_n/(n\theta))$. 在该文中, 当$\{\xi_n\}$是独立同分布或强平稳$\varphi$ -混合的正随机变量序列时,作者给出功率和$\{Y_n\}$用Wiener过程的强逼近结果.     

负相协误差下非线性模型$M$估计的强相合性

对于非线性模型$y_i=f(x_i,\theta)+e_i,\;i=1,2,\cdots,n$, 当$\{e_i,\,i=1,2,\cdots,n\}$为NA序列时, 本文在适当的条件下证明了$\theta$的$M$估计量的强相合性.     

NA样本的递归密度估计

设$\{X_n,n\geq 1\}$是一个严平稳的负相协的随机变量序列, 其概率密度函数为$f(x)$.本文讨论了$f(x)$的递归核估计量的联合渐近正态性.     

左截断相依数据下非参数回归的局部M 估计

本文对左截断模型, 利用局部多项式的方法构造了非参数回归函数的局部M 估计. 在观察样本为平稳α-混合序列下, 建立了该估计量的强弱相合性以及渐近正态性. 模拟研究显示回归函数的局部M 估计比Nadaraya-Watson 型估计和局部多项式估计更稳健.     

非灭绝条件下超过程的一些性质

在不灭绝的条件概率下的超过程简称为条件超过程. 考虑条件超过程(下临界或临界的情形)的一些性质. 首先, 对条件超过程总占位时测度在紧集上有限这一随机事件的概率给出了一个等价刻画, 并且给了这个等价刻画的一个应用. 我们的结果是已有结果从特殊分支机制 $r^{1+\beta}$到一般分支机制的推广. 还给出已有结果中一 个论断在 $d=3,4$ 时的新证明. 然后, 研究条件二分支超Brown运动的局部灭绝性质. 当$d=1$时, $\,X_t/\sqrt{t}\,$ 弱收敛到 $\eta\lambda$, 其中$\eta$ 是正的随机变量, $\lambda$是$\R$上的Lebesgue 测度; 当 $d\geq 2$ 时, 条件二分支超Brown运动 $\{X_t\}$ 在依概率意义下是局部灭绝的.     

左删失数据下ARMA(p,q)模型的估计

在观测数据左删失情形下由K—M估计方法得到,严平稳遍历序列{Xt}的均值和自协方差函数的估计,从而获得ARMA（p,q）模型的参数估计,且所给估计量是强相合估计．     

关于伪压缩映象不动点迭代的一点注记

设$K$是自反的并且具有一致Gateaux可微范数的Banach空间$E$的非空有界闭凸子集.设$T:K\rightarrow K$是一致连续的伪压缩映象.假设$K$的每一非空有界闭凸子集对非扩张映象具有不动点性质.设$\{\lambda_n\}$是$(0,\frac{1}{2}]$中序列满足: (i) $\lim_{n\rightarrow \infty}\lambda_n=0$; (ii) $\sum_{n=0}^{\infty}\lambda_n=\infty$.任给$x_1\in K$,定义迭代序列$\{x_n\}$为:$x_{n+1}=(1-\lambda_n)x_n+\lambda_nTx_n-\lambda_n(x_n-x_1),n\geq 1.$若$\lim_{n\rightarrow \infty}\|x_n-Tx_n\|=0$, 则上述迭代产生的$\{x_n\}$强收敛到$T$的不动点.     

一类正值线性过程的参数估计

在研究水质污染问题时,文[1]提出了非负一阶自回归模型:X_t=(?)X_(t-1)+ξ_t,其中{ξ_t}为独立同分布非负随机序列,0<(?)<1.此模型中 X_t 表示在时刻 t 时净化池中的污水量,1-(?)_1表示在单位时间间隔内被净化污水的比例,ξ_t 表示在时刻 t 注入净化池中的污水量.文[1]给出了模型参数的极为简便的强相合估计和相应的模拟结果.文[2]把[1]的结果推广到二阶自回归情形,克服了本质上的困难获得相应的结果.本文提出一类更为广泛的正值线性模型     

二维指数信号模型中参数Bootstrap估计的强相合性

本文主要研究了在统计信号处理当中具有广泛应用的二维带白噪声指数信号模型中参数估计的Bootstrap逼近, 借助于回归模型中Bootstrap逼近的构造方法, 给出了二维指数信号模型参数的自助估计, 并证明了自助估计具有强相合性. 最后采用了Monte-Carlo法对所提的方法进行随机模拟, 模拟的结果表明当噪声不服从正态分布时, Bootstrap方法的估计效果优于最小二乘估计.     

疏系数多维自回归模型的建立

§1.多维自回归模型的建立在实际问题中,我们经常需要处理多维量测数据.假设{X_t,1≤t≤N}是 k 维平稳序列,X_t=(x_(1t),x_(2t),…,x_(kt))~T,满足如下形式的多维自回归模型X_t=A_0+A_1X_(t-1)+…+A_pX_(t-p)+U_t,p相似文献   

OCCUPATION TIME PROCESSES OF FLEMING-VIOT PROCESSES

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UNIFORM CONVERGENCY FOR WEIGHTED PERIODOGRAM OF STATIONARY LINEAR RANDOM FIELDS

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Asymptotic Estimates of Finite-Time Ruin Probabilities with Dependent Risks and CMC Simulations

We consider a discrete-time risk model with dependence structures, where the claim-sizes \{X_n\}_{n\geq1} follow a one-sided linear process with independent and identically distributed (i.i.d.) innovations $\{\varepsilon_n\}_{n\geq1}$, and the innovations and financial risks form a sequence of independent and identically distributed copies of a random pair $(\varepsilon,Y)$ with dependent components. When the product \varepsilon Y has a heavy-tailed distribution, we establish some asymptotic estimates of the ruin probabilities in this discrete-time risk model. Finally, we use a Crude Monte Carlo (CMC) simulation to verify our results.     

Properties of kagi and renko moments for homogeneous diffusion processes

For a homogeneous diffusion process (X _t ) _t?0, we consider problems related to the distribution of the stopping times $\begin{gathered} \gamma _{\max } = \inf \{ t \geqslant 0:\mathop {\sup }\limits_{s \leqslant t} X_s - X_t \geqslant H\} ,\gamma _{\min } = \inf \{ t \geqslant 0:X_t - \mathop {\inf }\limits_{s \leqslant t} X_s \geqslant H\} , \hfill \\ \kappa _0 = \inf \{ t \geqslant 0:\mathop {\sup }\limits_{s \leqslant t} X_s - \mathop {\inf }\limits_{s \leqslant t} X_s \geqslant H\} . \hfill \\ \end{gathered} $ . The results obtained are used to construct an inductive procedure allowing us to find the distribution of the increments of the process X between two adjacent kagi and renko instants of time.     

关于$\{1,3,4\}$-逆的混合逆序律

In this paper, we study the mixed-type reverse order laws to {1, 3, 4}-inverses for closed range operators A, B and AB. It is shown that B{1, 3, 4}A{1, 3, 4} ?(AB){1, 3} if and only if R(A*AB) ? R(B). For every A(134)∈ A{1, 3, 4}, it has(A(134)AB){1, 3, 4}A{1, 3, 4} =(AB){1, 3, 4} if and only if R(AA*AB) ? R(AB). As an application of our results, some new characterizations of the mixed-type reverse order laws associated to the Moore-Penrose inverse and the {1, 3, 4}-inverse are established.     

cut* 空间的拓扑性质

该文引入了 cut^*空间的概念,所谓的 cut^*空间是指去掉任意一点连通,去掉任意两点不连通的连通空间.通过对其性质的讨论,得到如下主要结论: 首先得到cut^*空间中每个点非开即闭,并且cut^*空间中有无限多个闭点;其次讨论了一类特殊的 cut^*空间,即去掉一点是COTS的 cut^* 空间.指出``$X$是 cut^*空间,任意 $x\inX,X\setminus\{x\}$是不可约cut空间'这样的空间类是不存在的.在文章的最后,讨论了去掉一点是LOTS的 cut^*空间的覆盖性质,得到这样的空间是紧空间或Lindel\"of空间的结论.     

Asymptotic Behavior of the Fundamental Solutions for Critical Schrödinger Forms

Journal of Theoretical Probability - Let $$\{X_t\}_{t \ge 0}$$ be a transient $$\alpha $$ -stable process on $${\mathbb {R}}^d$$ and denote by H its generator. We consider the perturbation of the...     

广义复合二项风险模型的若干大偏差结果

This paper is a further investigation of large deviation for partial and random sums of random variables, where {Xn,n ≥ 1} is non-negative independent identically distributed random variables with a common heavy-tailed distribution function F on the real line R and finite mean μ∈ R. {N（n）,n ≥ 0} is a binomial process with a parameter p ∈ （0,1） and independent of {Xn,n ≥ 1}; {M（n）,n ≥ 0} is a Poisson process with intensity λ 〉 0, Sn = ΣNn i=1 Xi-cM（n）. Suppose F ∈ C, we futher extend and improve some large deviation results. These results can apply to certain problems in insurance and finance.     

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We study a birth and death process $\{N_t\}_{t\ge0}$ in i.i.d. random environment, for which at each discontinuity, one particle might be born or at most $L$ particles might be dead. Along with investigating the existence and the recurrence criterion, we also study the law of large numbers of $\{N_t\}$. We show that the first passage time can be written as a functional of an $L$-type branching process in random environment and a sequence of independent and exponentially distributed random variables. Consequently, an explicit velocity of the law of large numbers can be given.