含趋势项强相依非平稳序列的两个重要分布

{ηn}为平稳标准化正态序列,相关系数r|t-j|=Cov(ηu,ηj),若rnlogn→∞时,Leadbetter[1]等得到了序列最大值的渐近分布.本文考虑非平稳带有趋势项序列{ηn},得到了序列最大值的渐近分布和最大值与部分和的联合渐近分布.     

强相依非平稳高斯序列超过数点过程与部分和的联合渐近分布

设{Xi}∞i=1是标准化强相依非平稳高斯序列,记Sn=∑Xi,σn=√var(Sn),Mktn为X1,X2,…,Xtn的第k个最大值,Ntn为X1,X2,…,Xtn对水平μn(x)的超过数形成的点过程,tn是-列单调增加的正整数列,在一定条件下得到Ntn与Sn/σn,Mktn与Sn/σn的联合渐近分布.     

一类随机加权和的渐近性及其应用

本文考虑随机加权和及其最大值尾概率的渐近性,其中增量{X_i,i≥1}为一列独立同分布的实值随机变量,权重{θ_i,i≥1}为另一列非负的随机变量,并且两列随机变量满足某种相依结构.在增量的共同分布F属于控制变换分布族的条件下,我们得到了随机加权和及其最大值尾概率的弱渐近等价估计.特别地,当F属于一致变换分布族时,得到了渐近等价估计.最后,我们将该结果应用于破产概率的渐近估计.     

空间半参数变系数部分线性回归中的分位数估计

本文研究了空间数据变系数部分线性回归中的分位数估计. 模型中的参数估计量通过未知系数函数的分段多项式逼近得到, 而未知系数函数的估计量通过将参数估计量代入模型中并通过局部线性逼近得到. 文中推导了未知参数向量估计量的渐近分布, 并建立了未知系数函数估计量在内点及边界点的渐近分布. 通过Monte Carlo 模拟研究了估计量的有限样本性质.     

关于多维平稳序列的一个结果

是一个 m 维宽平稳随机向量序列,且 Ex_i=0(零向量),i=1,2,….易知(?)与 i 无关,且(?)(τ)~T 是一个 m 阶矩阵(A~T 表示 A 的转置矩阵).又(?)特别(?)(0)=(?)(0)~T 是每个随机向量的协方差阵.今有 n+1个 m 维宽平稳的随机向量 X_1,…,X_(n+1).令     

二元Cuadra-Auge型帕累托分布的相关性及渐近独立性

二元Cuadra-Auge型帕累托分布是一个二元对称分布,在本文中,分别讨论了二元Cuadra-Auge型帕累托分布的性质与相关性及渐近独立性,导出了二元Cuadra-Auge型帕累托分布的若干性质;证明了X,Y之间的相关系数介于零与四分之三之间,且X,Y之间渐近独立.     

B值随机元阵列加权和的Lr收敛性

设B是实可分的Banach空间,{Xni,Fni,un≤i≤vn,n≥1}是B值适应随机元阵 列,{αni,un≤i≤un,n≥1}是实数阵列,当0相似文献   

半参数回归模型的渐近有效L-估计

对半参数回归模型yi=χiTβ+g(χi)+ei,i=1,2,…,n,对非参数函数g(·)采用核估计的方法,构造了参数向量β的L-估计量λn,在一些正则条件下,获得了λn的渐近正态性和非参数函数g(·)的估计量gn(t)的最优收敛速度可达到O(n-(1/3)),并且给出了标准化L估计量λn的渐近分布的Berry-Esseen界.     

二元叠加码M_q~c(n,k,d)的性质

二元叠加码M_q~c(n,k,d)是二元叠加码M_q(n,k,d)的补阵,利用有限域F_2上向量的计算法则研究了二元叠加码M_q~c(n,k,d)的线性性质并证明了M_q~c(n,k,d)的析取(disjunct)性.     

一类二元相关威布尔分布及其参数估计

考虑生存函数为的二元威布尔分布,提出θ1,θ2,α,δ的估计并讨论了它们的渐近性,最后作模拟计算,得出了参数估计的渐近效．     

Bivariate inverse gaussian distribution

Summary A bivariate inverse Gaussian (IG) density function is constructed. Relations of the bivariate IG distribution to the normal and χ² distributions are established. The corresponding bivariate random walk (RW) density function is obtained. The properties and behaviour of bivariate IG distribution are studied for large parametric values. Moment estimates of the five parameters are given and applications are pointed out. A generalization to the multivariate IG distribution is proposed.     

Large deviations of bivariate Gaussian extrema

We establish sharp tail asymptotics for componentwise extreme values of bivariate Gaussian random vectors with arbitrary correlation between the components. We consider two scaling regimes for the tail event in which we demonstrate the existence of a restricted large deviations principle and identify the unique rate function associated with these asymptotics. Our results identify when the maxima of both coordinates are typically attained by two different versus the same index, and how this depends on the correlation between the coordinates of the bivariate Gaussian random vectors. Our results complement a growing body of work on the extremes of Gaussian processes. The results are also relevant for steady-state performance and simulation analysis of networks of infinite server queues.

     

二元竞争风险场合二元PL估计的弱收敛性及其Bootstrap结果

本文考虑了在二元竞争风险场合下二元PL估计的问题.文中给出了关于二元PL估计的独立同分布表达式,即将估计的差表为均值为零的独立同分布过程的和,且其余项的阶为O((u^-1logn)^3/4)a.s.,并证明该过程弱收敛到一个二元Gauss过程.关于Bootstrap情况也得到了类似的结果.     

On the residual dependence index of elliptical distributions

The residual dependence index of bivariate Gaussian distributions is determined by the correlation coefficient. This tail index is of certain statistical importance when extremes and related rare events of bivariate samples with asymptotic independent components are being modeled. In this paper we calculate the partial residual dependence indices of a multivariate elliptical random vector assuming that the associated random radius has distribution function in the Gumbel max-domain of attraction. Furthermore, we discuss the estimation of these indices when the associated random radius possesses a Weibull-tail distribution.     

Functional Non-Central and Central Limit Theorems for Bivariate Appell Polynomials

We consider quadratic forms in bivariate Appell polynomials involving strongly dependent time series. Both the spectral density of these time series and the Fourier transform of the kernel of the quadratic forms are regularly varying at the origin and hence may diverge, for example, like a power function. We obtain functional limit theorems for these quadratic forms by extending the recent results on the convergence of their finite-dimensional distributions. Some of these are functional central limit theorems where the limiting process is Brownian motion. Others are functional non-central limit theorems where the limiting processes are typically not Gaussian or, if they are Gaussian, then they are not Brownian motion.     

Estimation of two ordered bivariate mean residual life functions

Situations occur frequently in which the mean residual life (mrl) functions of two populations must be ordered. For example, if a mechanical device is improved, the mrl function for the improved device should not be less than that of the original device. Also, mrl functions for medical patients should often be ordered depending on the status of concomitant variables. This paper proposes nonparametric estimators of the bivariate mrl function under a mrl ordering. The estimators are shown to be asymptotically unbiased, strongly uniformly consistent and weakly convergent to a bivariate Gaussian process. The estimators are shown to be the projections, in a sense to be made precise, of the empirical mrl function onto an appropriate convex set of mrl functions. In the one-sample problem, the new estimators dominate the empirical mrl function in terms of risk with respect to a wide class of loss functions.     

Neighbourhoods of independence and associated geometry in manifolds of bivariate Gaussian and Freund distributions

We provide explicit information geometric tubular neighbourhoods containing all bivariate distributions sufficiently close to the cases of independent Poisson or Gaussian processes. This is achieved via affine immersions of the 4-manifold of Freund bivariate distributions and of the 5-manifold of bivariate Gaussians. We provide also the α-geometry for both manifolds. The Central Limit Theorem makes our neighbourhoods of independence limiting cases for a wide range of bivariate distributions; the topological character of the results makes them stable under small perturbations, which is important for applications in models of stochastic processes.      

Local times of stochastic processes with positive definite bivariate densities

A new class of stochastic processes, called processes of positive bivariate type, is defined. Such a process is typically one whose bivariate density functions are positive definite, at least for pairs of time points which are sufficiently mutually close. The class includes stationary Gaussian processes and stationary reversible Markov processes, and is closed under the operations of composition and convolution. The purpose of this work is to show that the local times of such processes can be investigated in a natural way. One of the main contributions is an orthogonal expansion of the local time which is new even in the well-studied stationary Gaussian case. The basic tool in its construction is the Lancaster-Sarmanov expansion of a bivariate density in a series of canonical correlations and canonical variables.     

Maxima and minima of independent and non-identically distributed bivariate Gaussian triangular arrays

In this paper, joint limit distributions of maxima and minima on independent and non-identically distributed bivariate Gaussian triangular arrays is derived as the correlation coefficient of ith vector of given nth row is the function of i/n. Furthermore, second-order expansions of joint distributions of maxima and minima are established if the correlation function satisfies some regular conditions.     

Models for Stationary Max-Stable Random Fields

Models for stationary max-stable random fields are revisited and illustrated by two-dimensional simulations. We introduce a new class of models, which are based on stationary Gaussian random fields, and whose realizations are not necessarily semi-continuous functions. The bivariate marginal distributions of these random fields can be calculated, and they form a new class of bivariate extreme value distributions.