负相依样本平滑移动过程的完全收敛性

设{Yi;－∞＜i＜∞}为一负相伴的同分布随机变量序列，{ai；－∞＜i＜∞}为绝对可和的实数序列。本文在适当的条件下。证明了平滑移动过程{∑k=1^n∑i=-∞^∞ai kYi/n^1/t;n≥1}的完全收敛性。     

相依样本下移动平滑过程的极限性质(英文)

定义平滑过程{Xn=∑i=-∞∞aiYi+n;n≥1},其中{ai;-∞i∞}是绝对可和的实数序列,{Yi;-∞i∞}是独立同分布并且-ρ-混合的双边无限序列.在{Yi;-∞i∞}均值为零、方差有限等条件下,证明移动平滑过程的收敛性,推广了李和张的结论.     

线性过程关于矩的重对数律的精确率

讨论线性过程Xk=∑∞i=-∞ai+kεi,其中{εi；-∞＜i＜∞}是均值为零,方差有限为σ2的双侧无穷独立同分布随机变量序列,{ai；-∞＜ i＜∞}为绝对可和的实数序列.令Sn=∑nl=1Xk,n≥1,假设|ε1|3＜∞,证明了对任意的δ＞-1,lim ∈↘0∈2δ+2∑∞n=1(㏒ ㏒ n)δ/n3/2㏒ nE{|Sn|-∈τ√2n ㏒ ㏒ n}+=√2τ√/√π(δ+1)(2δ+3)Γ(δ+2),其中τ2=σ2(∑∞i=-∞ai)2以及Γ(·)为Gamma函数.     

Precise asymptotics of complete moment convergence on moving average

Let {ξi,-∞i∞} be a doubly infinite sequence of identically distributed-mixing random variables with zero means and finite variances,{ai,-∞i∞} be an absolutely summable sequence of real numbers and X k =∑i=-∞+∞ aiξi+k be a moving average process.Under some proper moment conditions,the precise asymptotics are established for     

Precise Asymptotics in the Law of the Iterated Logarithm of Moving-Average Processes

In this paper, we discuss the moving-average process Xk = ∑i=-∞ ^∞ ai＋kεi, where {εi;-∞ 〈 i 〈 ∞} is a doubly infinite sequence of identically distributed ψ-mixing or negatively associated random variables with mean zeros and finite variances, {ai;-∞ 〈 i 〈 -∞） is an absolutely solutely summable sequence of real numbers.     

负相关样本平滑移动过程的矩完全收敛性(英文)

本文研究了负相关样本平滑移动过程Xk=∑∞i=-∞ai+kYi的矩完全收敛性,这里{Yi,-∞相似文献   

由φ-混合序列产生的长程相依过程的广义强逼近定理

设{X_k;k≥1}是由X_k=∑_(i=0)~βα_iε_(k-i)所定义的滑动平均过程,其中{ε_i;-∞i∞}是一同分布的φ-混合相依变量序列,{α_i;i≥0}为满足条件α_i~i~(-α)l(i)的实数序列,l(i)为一缓变函数.当1/2α1时,{X_k;k≥1}为一长程相依过程.在Eε_0~2可能为无穷的条件下,对长程相依过程{X_k;k≥1}的部分和建立了一个更为一般性的强逼近定理.     

A note on asymptotic behavior for negative drift random walk with dependent heavy-tailed steps and its application to risk theory

In this article, the dependent steps of a negative drift random walk are modelled as a two-sided linear process Xn =-μ ∞∑j=-∞ψn-jεj, where {ε, εn; -∞＜ n ＜ ∞}is a sequence of independent, identically distributed random variables with zero mean, μ＞0 is a constant and the coefficients {ψi;-∞＜ i ＜∞} satisfy 0 ＜∞∑j=-∞|jψj| ＜∞. Under the conditions that the distribution function of |ε| has dominated variation and ε satisfies certain tail balance conditions, the asymptotic behavior of P{supn≥0(-nμ ∞∑j=-∞εjβnj) ＞ x}is discussed. Then the result is applied to ultimate ruin probability.     

固定设计的时间序列半参数回归

考虑如下的半参数回归模型Yi=xTiβ+g(ti)+εi(0≤i≤n),其中{εi,0≤i≤n}和{εt,0≤t≤n}有相同的联合分布,{εt,-∞＜t＜∞}是具有零均值和有限方差σ2的严平稳α-混合时间序列.本文构造了上述模型中β,g(t)和σ2的局部多项式估计,在适当的条件下,得到了估计的渐近正态性和收敛速度.在一定的假设下,β的估计是自适应的,而且g(υ)(t)(g(t)的第υ阶导数)的估计的收敛速度是最优的.     

α-混合随机序列最近邻密度估计的强相合收敛速度

设{Xi）i=1^∞是一维平稳序列，具有公共的未知密度f（x），在{Xi}i=1^∞是α-混合的条件下，给出了f（x）基于前礼个观测值{Xi}i=1^∞的最近邻密度估计的强相合收敛速度，当f（x）满足适当条件，收敛速度可达到0（n^-1/3（ln n）^4（1＋p）／3））．     

Complete q-order moment convergence of moving average processes under φ-mixing assumptions

Let {Y _i , ?∞ < i < ∞} be a doubly infinite sequence of identically distributed φ-mixing random variables, and {a _i , ?∞ < i < ∞} an absolutely summable sequence of real numbers. We prove the complete q-order moment convergence for the partial sums of moving average processes $\left\{ {X_n = \sum\limits_{i = - \infty }^\infty {a_i + Y_{i + n} ,n \geqslant 1} } \right\}$ based on the sequence {Y _i , ?∞ < i < ∞} of φ-mixing random variables under some suitable conditions. These results generalize and complement earlier results.     

Complete moment convergence of moving average processes under <Emphasis Type="Italic">ρ</Emphasis>-mixing assumption

Let {Y _i : ?∞ < i < ∞} be a doubly infinite sequence of identically distributed ρ-mixing random variables, and {a _i : ?∞ < i < ∞} an absolutely summable sequence of real numbers. In this paper we prove the complete moment convergence for the partial sums of moving average processes \(\{ X_n = \sum\limits_{i = - \infty }^\infty {a_i Y_{i + n,} n \geqslant 1} \} \) based on the sequence {Y _i : ?∞ < i < ∞} of ρ-mixing random variables under some suitable conditions.     

Banach空间非扩张映像不动点强收敛定理

设E是具弱序列连续对偶映像自反Banach空间, C是E中闭凸集, T:C→ C是具非空不动点集F(T)的非扩张映像.给定u∈ C,对任意初值x0∈ C,实数列{αn}n∞=0,{βn}∞n=0∈ (0,1),满足如下条件:(i)sum from n=α to ∞α_n=∞, α_n→0;(ii)β_n∈[0,α) for some α∈(0,1);(iii)sun for n=α to ∞|α_(n-1) α_n|<∞,sum from n=α|β_(n-1)-β_n|<∞设{x_n}_(n_1)~∞是由下式定义的迭代序列:{y_n=β_nx_n (1-β_n)Tx_n x_(n 1)=α_nu (1-α_n)y_n Then {x_n}_(n=1)~∞则{x_n}_(n=1)~∞强收敛于T的某不动点.     

B值移动平均过程的Strassen重对数律

设 {ε,εt;t∈ Z}是 iid的 B值随机变量序列 ,{ aj;j∈ Z}是一个实数列 ,满足 ∞j=-∞|aj|<∞ .记 Xt= ∞j=-∞ajεt-j,Sn = nt=1Xt.对 p≥ 1 ,本文研究了n-1 -( p/ 2 ) (2 L2 n) -( p/ 2 ) ni=1 ‖ Si‖p 及 n-1 -( p/ 2 ) (2 L2 n) -( p/ 2 ) ni=0 ‖ Sn- Si‖ p的渐进性质 ,使得 Strassen(1 964)及 Chen(1 994)的一些结果得到推广 .     

PA随机变量产生的移动平均过程的矩完全收敛性

设{ε_i:-∞i∞}是同分布正相协随机变量序列,{a_i:-∞i+∞}是绝对可和的常数列.在一定的条件下得到了移动平均过程(?)的矩完全收敛的精确渐近性.把有关负相协条件下的结果推广到正相协情形.     

由非同分布NA序列产生的移动平均过程的完全收敛性

设{Y_n,-∞相似文献   

Hyperbolic Phenomena in a Degenerate Parabolic Equation

M. Bertsch and R. Dal Passo [1] considered the equation u_t = (φ(u)ψ(u_z))x., where φ > 0 and ψ is a strictly increasing function with lim_{s → ∞} ψ(s) = ψ_∞ < ∞. They have solved the associated Cauchy problem for an increasing initial function. Furthermore, they discussed to what extent the solution behaves like the solution of the first order conservation law u_t = ψ_∞(φ(u))_x. The condition φ > 0 is essential in their paper. In the present paper, we study the above equation under the degenerate condition φ(0) = 0. The solution also possesses some hyperbolic phenomena like those pointed out in [1].     

Regularity Results for a Strongly Degenerate Parabolic Equation

M. Bertsch & R. Dal Passo proved the existence and uniqueness of the Cauchy problem for u_t = (φ(u),ψ(u_x))_x, where φ > 0, ψ is a strictly increasing function with lim_{s → ∞}ψ(s) = ψ_∞ < ∞. The regularity of the solution has been obtained under the condition φ" < 0 or φ = const. In the present paper, under the condition φ" ≤ 0, we give some regularity results. We show that the solution can be classical after a finite time. Further, under the condition φ" ≤ -α_0 (where -α_0 is a constant), we prove the gradient of the solution converges to zero uniformly with respect to x as t → +∞.