关于Gauss过程样本轨道的性质

本文讨论具有平稳增量Gauss过程的不可微模，以及这类Gauss过程增量有多小的问题，并将有关Wiener过程的结果，在一定的条件下推广到这类Gauss过程中去．     

lp-值Wiener过程子列C-R型增量在H?lder范数下的泛函样本轨道性质

得到了l~p-值Wiener过程(1≤p∞)子列C-R型增量,在H?lder范数下的泛函样本轨道性质,推广了l~p-值Wiener过程的泛函重对数定律.     

关于N参数d维Ornstein-Uhlenbeck过程样本轨道性质的注记

设{W(s),s∈R₊^N)是N参数Wiener过程,定义N参数Ornstein-Uhlenbeck过程如下:作X^N,d={(x¹(t),…,X^d(f)),t∈R₊^N),这里Sⁱ是1≤i≤d两两独立同分布的N参数OUP称之为N参数d维OUP.本文我们证明了X^N,d象集的d维Lebesgue测度为零。     

广义 Lévy单的样本轨道性质

设X(t)是下指数为α取值于R^d的N参数广义Lévy 单, R={(x,t]=∏^N_i=1 (s_i,t_i], s_i<t_i}, E(x, Q)={t∈Q: X(t)=x}, Q∈∏, 是 X在点x处的水平集, X(Q)={x: 设X(t)是下指数为α取值于R^d的N参数广义Lévy 单, R={(x,t]=∏^N_i=1 (s_i,t_i], s_i<t_i}, E(x, Q)={t∈Q: X(t)=x}, Q∈∏, 是 X在点x处的水平集, X(Q)={x: 设X(t)是下指数为α取值于Rd的N参数广义Lévy单,R={(s,t]=∏Ni=1(si,ti],si＜ti},E(x,Q)={t∈Q∶X(t)=x},Q∈R,是X在点x处的水平集,X(Q)={x∶(∈)t∈Q,使得X(t)=x}为X在Q上的像集.本文探讨了X(t)局部时存在性及其增量的大小.同时,也得到了水平集E(x,Q)Hausdorff维数和X(Q)一致维数上界的结果.     

广义Lévy单的样本轨道性质

设X(t)是下指数为α取值于R~d的N参数广义Lévy单,■={(s,t]=∏(s_i,t_i],s_i＜t_i},E(x,Q)={t∈Q:X(t)=x},Q∈■,是X在点x处的水平集,X(Q)={x:■t∈Q,使得X(t)=x}为X在Q上的像集．本文探讨了X(t)局部时存在性及其增量的大小．同时,也得到了水平集E(x,Q)Hausdorff维数和X(Q)一致维数上界的结果．     

l~∞－值Gauss过程的增量有多大

本文在一定条件下给出了ｌ∞－值Ｇａｕｓｓ过程增量有多大，并把它应用于一类ｌ∞－值Ｏｒｎｓｔｅｉｎ－Ｕｈｌｅｎｂｅｃｋ过程上，给出相应结论．     

l p值Gauss过程的增量有多大

设{Y(t),t≥0}={X_k(t),t≥0}^∞_k=1是独立的Gauss过程序列,σ²_k(h)=E(X_k(t+h)-X_k(t))².记σ(p,h)=(sum from k=1 to ∞ σ^p_k(h))^1/p,P≥1.考察σ(P,h)有界时Y(·)的大增量.作为一个例子,给出了无穷维分数Ornstein-Uhlenbeck过程在l^p空间中的大增量.所建立的方法适用于某些其它类型的平稳增量过程.     

关于l^P值Gauss过程的快点

该文证明了l^p值Gauss过程发生无限次例外振动的点集是一随机分形，并且给出了点击概率的一个临界值.     

N参数d维Ornstein-Uhlenbeck过程样本轨道的一个性质

对于N参数d维Ornstein-Uhlenbeek过程,本文借助于[2]中的方法,证明了在d<2N时,{X_(N, d)(t):t∈B_ ~N}的d维Lebesgue测度大于零。     

退火过程的轨道性质

本文中我们建立了一些非时齐过程的大偏差性质．利用大偏差技术，我们找到了退火过程的ω－极限集．     

Spectral properties of processes derived from stationary Gaussian sequences

Many qualitative properties of the spectral measure of a stationary Gaussian sequence are spectral properties of the underlying shift transformation. This has implications in time series analysis.     

Small values of Gaussian processes and functional laws of the iterated logarithm

Summary We estimate small ball probabilities for locally nondeterministic Gaussian processes with stationary increments, a class of processes that includes the fractional Brownian motions. These estimates are used to prove Chung type laws of the iterated logarithm.Research supported by the United States Air Force office of Scientific Research, Contract No. 91-0030     

On Toeplitz type quadratic functionals of stationary Gaussian processes

Summary A central limit theorem for Toeplitz type quadratic functionals of a stationary Gaussian processX(t),t, is proved, generalizing the result of Avram [1] for discrete time processes. The result is applied to the problem of nonparametric estimation of linear functionals of an unknown spectral density function. We give some upper bounds for the minimax mean square risk of the nonparametric estimators, similar to those by Ibragimov and Has'minskii [12] for a probability density function.     

Limit distributions for the maxima of stationary Gaussian processes

Let {X_n} be a stationary Gaussian sequence with E{X₀} = 0, {X²₀} = 1 and E{X₀X_n} = rⁿ_n Let c_n = (2ln n)^{$\text{built1}\text{2}$}, b_n = c_n? $\text{1}\text{2}$c^-1_n ln(4π ln n), and set M_n = max_{0 ?k?n}X_k. A classical result for independent normal random variables is that

$\text{P[c}{}_{\mathrm{n}}\text{(M}{}_{\mathrm{n}}\text{?b}{}_{\mathrm{n}}\text{)?x]→exp[-e}{}^{\mathrm{-x}}\text{] as n → ∞ for all x.}$

Berman has shown that (1) applies as well to dependent sequences provided r_nlnn = o(1). Suppose now that {r_n} is a convex correlation sequence satisfying r_n = o(1), (r_nlnn)^-1 is monotone for large n and o(1). Then

$\text{P[r}{}_{\mathrm{n}}{}^{\mathrm{<mtext>-1</mtext><mtext>2</mtext>}}\text{(M}{}_{\mathrm{n}}\text{? (1?r}{}_{\mathrm{n}}\text{)}{}^{\mathrm{<mtext>1</mtext><mtext>2</mtext>}}\text{b}{}_{\mathrm{n}}\text{)?x] → Ф(x)}$

for all x, where Ф is the normal distribution function. While the normal can thus be viewed as a second natural limit distribution for {M_n}, there are others. In particular, the limit distribution is given below when r_n is (sufficiently close to) γ/ln n. We further exhibit a collection of limit distributions which can arise when r_n decays to zero in a nonsmooth manner. Continuous parameter Gaussian processes are also considered. A modified version of (1) has been given by Pickands for some continuous processes which possess sufficient asymptotic independence properties. Under a weaker form of asymptotic independence, we obtain a version of (2).     

Functional limit theorems for generalized quadratic variations of Gaussian processes

In this paper, we establish functional convergence theorems for second order quadratic variations of Gaussian processes which admit a singularity function. First, we prove a functional almost sure convergence theorem, and a functional central limit theorem, for the process of second order quadratic variations, and we illustrate these results with the example of the fractional Brownian sheet (FBS). Second, we do the same study for the process of localized second order quadratic variations, and we apply the results to the multifractional Brownian motion (MBM).     

Reduced-load equivalence for Gaussian processes

We consider a fluid model fed by two Gaussian processes. We obtain necessary and sufficient conditions for the workload asymptotics to be completely determined by one of the two processes, and apply these results to the case of two fractional Brownian motions.     

Large deviations of infinite intersections of events in Gaussian processes

Consider events of the form {_Zs≥ζ(s),s∈S}$\{Z_s\ge \zeta \left(s\right),s\in S\}$, where Z$Z$ is a continuous Gaussian process with stationary increments, ζ$\zeta$ is a function that belongs to the reproducing kernel Hilbert space R$R$ of process Z$Z$, and S⊂R$S\subset \mathbb{R}$ is compact. The main problem considered in this paper is identifying the function β^∗∈R${\beta }^{\ast }\in R$ satisfying β^∗(s)≥ζ(s)${\beta }^{\ast }\left(s\right)\ge \zeta \left(s\right)$ on S$S$ and having minimal R$R$-norm. The smoothness (mean square differentiability) of Z$Z$ turns out to have a crucial impact on the structure of the solution. As examples, we obtain the explicit solutions when ζ(s)=s$\zeta \left(s\right)=s$ for s∈[0,1]$s\in [0,1]$ and Z$Z$ is either a fractional Brownian motion or an integrated Ornstein–Uhlenbeck process.     

Continuity of l 2 two-parameter ornstein-uhlenbeck processes

Let {Y(t);t=(t ₁,t₂)≥0}={X_k(t₁,t₂);t₁≥0,t₂≥0} _k=1 ^∞ , be a sequence of two-parameter Ornstein-Uhlenbeck processes (OUP₂) with coefficient a _k>0,ß_k>0.. A Fernique type inequality is established and the sufficient condition for a. s. l ² continuity of Y(?) is studied by means of the inequality.     

Small ball probabilities of Gaussian fields

Summary Lower bounds on the small ball probability are given for Brownian sheet type Gaussian fields as well as for general Gaussian fields with stationary increments in ^d . In particular, a sharp bound is found for the fractional Lévy Brownian fields.The research is partly supported by a National University of Singapore's Research Project