φ-混合序列的随机中心极限定理

设{Xn,n≥1}为严平稳的φ-混合序列,{N_-n,n≥1}为一列非负整值随机变量序列,且与{X_n,n≥1}独立,随机部分和为S_N_n=Nn∑ =1X_i,在适当的假设条件下,利用φ混合序列的极限性质,证明了严平稳φ混合序列的随机中心极限定理,得到了Tn=S_N_n-ES_N_n/Var(S_N_n)^(1/2)依分布收敛于T(Z_1,Z_2),其中T(Z_1,Z_2)为Z_1和Z_2的线性函数,Z_1～N(0,1),Z_2为{N_n,n≥1}正则化后的极限分布.

B值m相依随机元序列的随机指标中心极限定理

讨论 B值 m相依随机元序列的随机指标中心极限定理 ,给出其成立的一个充分条件 ,同时给出当 B是 2型空间时随机指标中心极限定理 .

B值m相依随机元列移动平均过程的随机指标中心极限定理

设{εt;t∈Z}是均值为零、二阶矩有限的B值m相依随机元列,{aj;j∈Z}是一实数序列,并且∑∞j=-∞aj<+∞.定义移动平均过程Xt=∑∞j=-∞ajεt-j(t≥1).利用Beveridge-Nelson分解及{εt;t≥1}的弱收敛定理,给出{Xt;t≥1}满足随机指标中心极限定理的充分条件.

LPQD序列的随机指标中心极限定理

设{X_n,n≥1}为严平稳的线性正象限相依(LPQD)序列,{N_n,n≥1}为一列非负整数值随机变量序列,且与{X_n,n≥1}独立.记随机部分和为S_N_n=N_n∑i=1 X_i,在适当的假设条件下,利用LPQD序列的极限性质,证明严平稳LPQD序列的随机指标中心极限定理和Berry-Esseen界.

B值平稳线性过程的迭对数律及随机指标中心极限定理

设 { εt;t∈Z}是独立同分布的 B值随机元序列 ,aj;j∈ Z是一实数序列 ,并且 ∞j=-∞| aj| <∞ ,定义平稳线性过程 Xt= ∞j=-∞ajεt- j.本文研究 { Xt;t∈ IN }部分和序列的收敛性质和极限定理 ,给出了 { Xt;t∈ IN }满足有界迭对数律。

强平稳NA序列的随机指标中心极限定理

计论了强平稳NA序列的随机指标中心极限定理，给出其成立的一个充分条件．

泊松分布样本均值与方差之差的渐近正态性

基于多维随机向量的中心极限定理及delta方法,证明泊松分布的样本均值与方差之差的渐近正态性。

Characteristic Functions over C*-Probability Spaces

Various properties of the characteristic functions of random variables in a non-commutative C*-probability space are studied in this paper. It turns out that the distributions of random variables are uniquely determined by their characteristic functions. By using the properties of characteristic functions, a central limit theorem for a sequence of independent identically distributed random variables in a C*-probability space is established as well.

A RANDOM FUNCTIONAL CENTRAL LIMIT THEOREM FOR PROCESSES OF PRODUCT SUMS OF LINEAR PROCESSES GENERATED BY MARTINGALE DIFFERENCES

A random functional central limit theorem is obtained for processes of partial sums andproduct sums of linear processes generated by non-stationary martingale differences. It devel-ops and improves some corresponding results on processes of partial sums of linear processesgenerated by strictly stationary martingale differences, which can be found in [5].

Moment bounds for IID sequences under sublinear expectations

With the notion of independent identically distributed(IID) random variables under sublinear expectations introduced by Peng,we investigate moment bounds for IID sequences under sublinear expectations. We obtain a moment inequality for a sequence of IID random variables under sublinear expectations. As an application of this inequality,we get the following result:For any continuous functionsatisfying the growth condition |(x) | C(1 + |x|p) for some C > 0,p 1 depending on ,the central limit theorem under sublinear expectations obtained by Peng still holds.

Local linear estimator for stochastic diferential equations driven by α-stable Lvy motions

We study tile local linear estimator for tile drift coefficient of stochastic differential equations driven by α-stable Levy motions observed at discrete instants. Under regular conditions, we derive the weak consis- tency and central limit theorem of the estimator. Compared with Nadaraya-Watson estimator, the local linear estimator has a bias reduction whether the kernel function is symmetric or not under different schemes. A silnu- lation study demonstrates that the local linear estimator performs better than Nadaraya-Watson estimator, especially on the boundary.

PARAMETER ESTIMATION FOR A DISCRETELY OBSERVED STOCHASTIC VOLATILITY MODEL WITH JUMPS IN THE VOLATILITY

In this paper a stochastic volatility model is considered. That is, a log price process Y whichis given in terms of a volatility process V is studied. The latter is defined such that the logprice possesses some of the properties empirically observed by Barndorff-Nielsen & Jiang[6]. Inthe model there are two sets of unknown parameters, one set corresponding to the marginaldistribution of V and one to autocorrelation of V. Based on discrete time observations ofthe log price the authors discuss how to estimate the parameters appearing in the marginaldistribution and find the asymptotic properties.