关于记录值序列部分和的渐近正态性

证明了Burr分布和Frechet分布的记录值序列的部分和是渐进对数正态的，从而解决了[2]中的一个悬而未决的问题。     

不同分布NA列加权和的强极限定理及其在线性模型中的应用

本文讨论了不同分布ＮＡ列Ｓｔｏｕｔ型加权和的完全收敛性和强稳定性，推广并改进了Ｓｔｏｕｔ关于ｉｉｄ列的相应结果，从而将赵林城关于独立误差的方差估计的强收敛速度的理想结果推广到ＮＡ误差的场合。     

关于记录值序列部分和的渐近正态性

证明了 Burr分布和 Frechét分布的记录值序列的部分和是渐进对数正态的 ,从而解决了文[2 ]中的一个悬而未决的问题     

鞅差序列加权和的中心极限定理

对一类鞅差序列,我们获得了其加权和中心极限定理,并给出了在非线性回归模型及线性模型中的应用。     

线性过程关于大数律的精确渐近性

该文主要讨论的是滑线性过程 $X_k=\sum\limits_{i=-\infty}^\infty a_{i+k}\varepsilon_i$,其中 $\{\varepsilon_i; -\infty$\varphi$ -混合或负相伴随机变量序列,$\{a_i;-\inftyp$, 若 $E|\varepsilon_1|^r<\infty$$\lim_{\epsilon\searrow 0}\epsilon^{2(r-p)/(2-p)}\sum\limits_{n=1}^\infty n^{r/p-2}P\{|S_n|\geq \epsilonn^{1/p}\}=\frac{p}{r-p}E|Z|^{2(r-p)/(2-p)},$ 其中 $Z$ 是服从均值为零,方差为 $\tau^2=\sigma^2\cdot(\sum\limits_{i=-\infty}^\infty a_i)^2$的正态分布.     

加权和线性过程的渐近正态性（英文）

本文建立了α-混合序列情形的加权和平稳线性过程的渐近正态性.获得的结论基于最少的权条件.所得结论将Abadir等[Econometric Theory,2014,30(1):252-284]中的结论推广至α-混合序列情形.     

部分线性模型中估计的渐近正态性

考虑回归模型其中是未知函数，（ｘ＿ｉ，ｔ＿ｉ，ｕ＿ｉ）是固定非随机设计点列，β是待估参数，ｅ＿ｉ是随机误差。基于ｇ（·）及ｆ（·）的一类非参数估计（包括常见的核估计和近邻估计），我们构造了β的加权最小二乘估计，并证得了最小二乘估计和加权最小二乘估计的渐近正态性。     

误差为FCA过程的线性模型HD估计的渐近正态性

考虑线性回归模型y=x^Tβ+e₁其中误差e是函数系数自回归（FCA）过程.本文研究该模型未知参数的Huber-Dutter估计的渐近性质,在合理的条件下,证明了这些估计量以n^-（1/2）速度渐近于正态分布.     

强混合样本下回归加权估计的一致渐近正态性

在强混合样本下,讨论固定设计回归模型的加权函数估计的一致渐近正态性,给出一致渐近正态性的收敛速度,这个速度接近n-1／6．     

相依序列加权和的几乎处处中心极限定理

该文讨论了非平稳负(正)相依序列加权和的几乎处处中心极限定理,改进并推广了相依序列几乎处处中心极限定理的相关结果.     

Asymptotic Normality for Non-linear Functionals of Non-causal Linear Processes with Summable Weights

Let be a non-causal linear process with weights a_js satisfying certain summability conditions, and the iid sequence of innovation {_i} having zero mean and finite second moment. For a large class of non-linear functional K which includes indicator functions and polynomials, the present paper develops the central limit theorem for the partial sums      

A Note on the Asymptotic Normality of Sample Autocorrelations for a Linear Stationary Sequence

We consider a stationary time series {X_t} given byX_t=∑^∞_k=−∞ ψ_kZ_t−k, where {Z_t} is a strictly stationary martingale difference white noise. Under assumptions that the spectral densityf(λ) of {X_t} is squared integrable andm^τ ∑_|k|?m ψ²_k→0 for someτ>1/2, the asymptotic normality of the sample autocorrelations is shown. For a stationary long memoryARIMA(p, d, q) sequence, the conditionm^τ ∑_|k|?m ψ²_k→0 for someτ>1/2 is equivalent to the squared integrability off(λ). This result extends Theorem 4.2 of Cavazos-Cadena [5], which were derived under the conditionm ∑_|k|?m ψ²_k→0.     

An Almost Sure Central Limit Theorem for Weighted Sums of Mixing Sequences

In this paper, we prove an almost sure central limit theorem for weighted sums of mixing sequences of random variables without stationary assumptions. We no longer restrict to logarithmic averages, but allow rather arbitrary weight sequences. This extends the earlier work on mixing random variables.     

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设{Xni,un≤i≤vn,n≥1}与{ani,un≤i≤vn,n≥1}分别为一个随机阵列和一个常数阵列.本文首先引入了随机阵列{Xni,un≤i≤vn,n≥1}关于常数阵列{ani,un≤i≤vn,n≥1}剩余h-可积的概念,它是弱于h-可积,Cesaroα-可积等其它相关可积的定义.然后在这一可积的定义和适当的条件下,我们研究了相依随机序列加权和的强收敛性和平均收敛性,推广并改进了相关文献已有结果.     

Almost Sure Central Limit Theorems for Heavily Trimmed Sums

We obtain an almost sure central limit theorem (ASCLT) for heavily trimmed sums. We also prove a function-typed ASCLT under the same conditions that assure measurable functions to satisfy the ASCLT for the partial sums of i.i.d, random variables with E X1 = 0, EX1^2 = 1.     

Sharp Conditions for the CLT of Linear Processes in a Hilbert Space

In this paper we study the behavior of sums of a linear process associated to a strictly stationary sequence with values in a real separable Hilbert space and are linear operators from H to H. One of the results is that satisfies the CLT provided are i.i.d. centered having finite second moments and . We shall provide an example which shows that the condition on the operators is essentially sharp. Extensions of this result are given for sequences of weak dependent random variables under minimal conditions.     

Asymptotic Normality of Kernel Type Density Estimators for Random Fields

Kernel type density estimators are studied for random fields. It is proved that the estimators are asymptotically normal if the set of locations of observations become more and more dense in an increasing sequence of domains. It turns out that in our setting the covariance structure of the limiting normal distribution can be a combination of those of the continuous parameter and the discrete parameter cases. The proof is based on a new central limit theorem for α-mixing random fields. Simulation results support our theorems. Final version 29 October 2004     

Asymptotic Independence of Distant Partial Sums of Linear Processes

We investigate the joint weak convergence (f.d.d. and functional) of the vector-valued process (U _n ⁽¹⁾ (τ), U _n ⁽²⁾ (τ)) for τ ∈ [0, 1], where and are normalized partial-sum processes separated by a large lag m, m/n → ∞, and (X _t , t ∈ ℤ) is a stationary moving-average process with i.i.d. (or martingale-difference) innovations having finite variance. We consider the cases where (X _t ) is a process with long memory, short memory, or negative memory. We show that, in all these cases, as n → ∞ and m/n → ∞, the bivariate partial-sum process (U _n ⁽¹⁾ (τ), U _n ⁽²⁾ (τ)) tends to a bivariate fractional Brownian motion with independent components. The result is applied to prove the consistency of certain increment-type statistics in moving-average observations. This work supported by the joint Lithuania-French research program Gilibert. __________ Translated from Lietuvos Matematikos Rinkinys, Vol. 45, No. 4, pp. 479–500, October–December, 2005.     

部分和乘积的几乎处处中心极限定理

设X_n, n≥1是独立同分布正的随机变量序列, E(X₁)=u >0, Var(X₁)=σ², E|X₁|³<∞, 记S_n==∑^N_k=1X_k, 变异系数γ=σ/u.g是满足一定条件的无界可测函数, 证明了 lim_N→∞1/logN∑^N_n=11/n g((∏ⁿ_k=1S_k/n!uⁿ )^1/γ√n )=∫^∞₀g(x)dF(x),a.s., 其中 F(•) 是随机变量e^√2ξ 的分布函数, ξ 是服从标准正态分布的随机变量.