加权和线性过程的渐近正态性

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.     

ARCH(p)模型样本均值与样本自相关函数的渐近正态性

利用严平稳m步相依序列中心极限定理证明了ARCH(p)模型样本均值与样本自相关函数的渐近正态性质.     

Asymptotic Joint Normality of Outdegrees of Nodes in Random Recursive Trees

We study the joint probability distribution of the number of nodes of outdegree 0, 1, and 2 in a random recursive tree. We complete the known partial list of exact means and variances for outdegrees up to two by obtaining exact combinatorial expressions for the remaining means, variances, and covariances. The joint probability distribution of the number of nodes of outdegree 0, 1, and 2 is shown to be asymptotically trivariate normal and the asymptotic covariance structure is explicitly determined. It is also shown how to extend the results (at least in principle) to obtain a limiting multivariate normal distribution for nodes of outdegree 0, 1, …, k.     

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 applications of the martingale central limit theorem to random permutations

A unified martingale approach is presented for establishing the asymptotic normality of some sequences of random variables. It is applied to the numbers of inversions, rises, and peaks, respectively, as well as the oscillation and the sum of consecutive pair products of a random permutation. © 1997 John Wiley & Sons, Inc. Random Struct. Alg., 10, 323–332 (1997)     

The Asymptotic Distribution of Sample Autocorrelations for a Class of Linear Filters

We consider a stationary time series {X_t} given by X_t = Σ_kψ_kZ_{t − k}, where the driving stream {Z_t} consists of independent and identically distributed random variables with mean zero and finite variance. Under the assumption that the filtering weights ψ_k are squared summable and that the spectral density of {X_t} is squared integrable, it is shown that the asymptotic distribution of the sequence of sample autocorrelation functions is normal with covariance matrix determined by the well-known Bartlett formula. This result extends classical theorems by Bartlett (1964, J. Roy Statist. Soc. Supp.8 27-41, 85-97) and Anderson and Walker (1964, Ann. Math. Statist.35 1296-1303), which were derived under the assumption that the filtering sequence {ψ_k] is summable.     

线性过程误差下的非参数回归模型的注记

对于非参数回归模型Yni=g(xni)+εni,1in,用一般非参数方法,定义了未知函数g(.)的估计量gn(x),当误差序列{εni,1in}为一弱平稳线性过程序列时,在一定条件下,获得了估计量gn(x)的一致强相合性.     

A Note on Asymptotic Normality of Kernel Estimation for Linear Random Fields on <Emphasis Type="Italic">Z</Emphasis><Superscript>2</Superscript>

This note considers the kernel estimation of a linear random field on Z ². Instead of imposing certain mixing conditions on the random fields, it is assumed that the weights of the innovations satisfy a summability property. By building a martingale decomposition based on a suitable filtration, asymptotic normality is proven for the kernel estimator of the marginal density of the random field. T.-L. Cheng’s research is supported in part by NSC 94-2118-M-018-001, Taiwan. Also, he is indebted to Department of Mathematics and Statistics, University of Calgary, for their hospitality during his visit. X. Lu’s research is supported in part by NSERC Discovery Grant of Canada.     

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     

非齐次马氏链的中心极限定理

本文将针对非齐次马氏链的转移矩阵列在Ces`aro收敛意义下,利用鞅的中心极限定理证明一个不同于Dobrushin结果的非齐次马氏链的中心极限定理。     

渐近鞅差序列的大数定律

研究渐近鞅的收敛性及渐近鞅的 Doob分解 ,证明了渐近鞅差序列服从大数定律     

Asymptotic behavior of a tagged particle in simple exclusion processes

We review in this article central limit theorems for a tagged particle in the simple exclusion process. In the first two sections we present a general method to prove central limit theorems for additive functional of Markov processes. These results are then applied to the case of a tagged particle in the exclusion process. Related questions, such as smoothness of the diffusion coefficient and finite dimensional approximations, are considered in the last section.     

Asymptotic Approximation with a Sequence of Positive Linear Operators

The rate of pointwise convergence for a sequence of positive linear operators L_n approximating continuous functions on a finite interval is considered. The complete asymptotic expansion for the operators L_n as n tends to infinity is presented. It turns out that the central factorial numbers of first and second kind play an important role in the asymptotic expansion. The present work is an extension to that reported by Ivan and Raa.     

Local Asymptotic Normality for Linear Homogeneous Difference Equations with Non-Gaussian Noise

This paper considers the problem of estimation of drift parameter for linear homogeneous stochastic difference equations. The Local Asymptotic Normality (LAN) for the problem is proved. LAN implies the Hajek–Le Cam minimax lower bound. In particular, it is shown that the Fisher's information matrix for the problem can be expressed in terms of the stationary distribution of an auxiliary Markov chain on the projective space P(^d).     

Asymptotic behaviors for percolation clusters with uncorrelated weights

We consider random processes occurring on bond percolation clusters and represented as a generalization of the “divide and color model” introduced by Häggström in 2001. We investigate the asymptotic behaviors for bond percolation clusters with uncorrelated weights. For subcritical and supercritical phases, we prove the law of large numbers and central limit theorems in the models corresponding to the so-called quenched and annealed probabilities.     

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。     

一类非齐次树上的非齐次马尔可夫链的若干强极限定理

极限定理一直是国际概率论界研究的中心课题之一.通过构造适当的辅助非负鞅而给出了一类特殊非齐次树上可列状态的非齐次马尔可夫链场的若干强极限定理.     

任意随机变量序列的一个强极限定理

研究任意随机变量序列的强收敛性.利用鞅差序列级数收敛定理,证明了任意随机序列的一个强极限定理,作为推论,得到了马氏过程、鞅差序列及独立随机变量序列的强大数定律.     

ON THE RATES OF CONVERGENCE IN THE CENTRAL LIMIT THEOREM FOR TWO－PARAMETER MARTINGALE DIFFERENCES

ＯＮＴＨＥＲＡＴＥＳＯＦＣＯＮＶＥＲＧＥＮＣＥＩＮＴＨＥＣＥＮＴＲＡＬＬＩＭＩＴＴＨＥＯＲＥＭＦＯＲＴＷＯ－ＰＡＲＡＭＥＴＥＲＭＡＲＴＩＮＧＡＬＥＤＩＦＦＥＲＥＮＣＥＳ￥（龙红卫）ＬｏｎｇＨｏｎｇｗｅｉ（ＩｎｓｔｉｔｕｔｅｏｆＭａｔｈｅｍａｔｉｃａｌ...     

On the Weak Invariance Principle for Stationary Sequences under Projective Criteria

In this paper, we study the central limit theorem and its weak invariance principle for sums of a stationary sequence of random variables, via a martingale decomposition. Our conditions involve the conditional expectation of sums of random variables with respect to the distant past. The results contribute to the clarification of the central limit question for stationary sequences. Magda Peligrad is supported in part by a Charles Phelps Taft research support grant at the Univeristy of Cincinnati and the NSA grant H98230-05-1-0066.