Fractional noise

Fractional noiseN(t),t 0, is a stochastic process for every , and is defined as the fractional derivative or fractional integral of white noise. For = 1 we recover Brownian motion and for = 1/2 we findf ^–1-noise. For 1/2 1, a superposition of fractional noise is related to the fractional diffusion equation.     

硬化非高斯结构响应首次穿越的Monte Carlo模拟

在对比分析已有硬化非高斯模型(Winterstein硬化模型、Ding和Chen模型)的基础上,提出了一个基于Zhao和Lu模型的新硬化非高斯模型.新模型预测偏度和峰度的误差比既有硬化模型小,且最大误差分别为0.311和0.479,表明新模型具有良好的精度;同时新模型扩展了Zhao和Lu模型的适用范围.最后运用新硬化模型模拟硬化非高斯过程样本,发展了硬化非高斯结构响应首次穿越的Monte Carlo模拟方法.数值算例验证了本文方法用于硬化非高斯结构响应首次穿越失效概率计算有较高的精度;杭州新火车东站大跨屋盖以及南水北调工程渡槽结构工程实例说明了本文方法的使用过程.     

Extinction Effects of Multiplicative Non-Gaussian L′evy Noise in a Tumor Growth System with Immunization

The extinction phenomenon induced by multiplicative non-Gaussian Levy noise in a tumor growth model with immune response is discussed. Under the influence of the stochastic immune rate, the model is analyzed in terms of a stochastic differential equation with multiplicative noise. By means of the theory of the infinitesimal generator of Hunt processes, the escape probability, which is used to measure the noise-induced extinction probability of tumor cells, is explicitly expressed as a function of initial tumor cell density, stability index and noise intensity. Based on the numerical calculations, it is found that for different initial densities of tumor cells, noise parameters play opposite roles on the escape probability. The optimally selected values of the multiplicative noise intensity and the stability index are found to maximize the escape probability.     

Limit Gaussian Behavior of the Solutions of the Multidimensional Burger'; Equation with Weak-Dependent Initial Conditions

Asymptotic normality of the solutions of the initial-value problems for multidimensional Burgers' equation with weakly dependent possible non-Gaussian initial condition is proved.     

非高斯型互相关随机数的发生技术

在用蒙特卡罗法进行仿真研究（例如进行测量不确定度评定）时,常常需要发生多个非高斯型互相关的随机数.就这一问题,给出完整的解决方案：用Hermite展开式生成近似的非高斯变量,借助Cholesky分解建立各变量之间的相关性.方法的关键在于对互相关系数矩阵进行“预变形”,使Cholesky分解也适用于非高斯变量.此外,还利用Cholesky分解式下三角矩阵的特点,对矩调整和建立相关性两个过程进行解耦.给出了详细的算法说明.     

Multiple resonances with time delays in scale-free networks of Hodgkin-Huxley neurons subjected to non-Gaussian noise

The spiking behavior with varying time delay in scale-free networks of Hodgkin-Huxley neurons with non-Gaussian noise has been studied,and the effect of non-Gaussian noise on the delay-induced spiking behavior is discussed. It was found that multiple spatio-temporal resonances occur when the delay lengths are integer multiples of the spiking periods,and the resonances may be strengthened when the non-Gaussian noise is appropriate. This result shows that time delays can optimize the spiking temporal regulari...     

Oracle Inequalities for Efromovich–Pinsker Blockwise Estimates

Oracle inequality is a relatively new statistical tool for the analysis of nonparametric adaptive estimates. Oracle is a good pseudo-estimate that is based on both data and an underlying estimated curve. An oracle inequality shows how well an adaptive estimator mimics the oracle for a particular underlying curve. The most advanced oracle inequalities have been recently obtained by Cavalier and Tsybakov (2001) for Stein type blockwise estimates used in filtering a signal from a stationary white Gaussian process. The authors also conjecture that a similar result can be obtained for Efromovich–Pinsker (EP) type blockwise estimators where their approach, based on Stein's formula for risk calculation, does not work. This article proves the conjecture and extends it upon more general models which include not stationary and dependent processes. Other possible extensions, a discussion of practical implications and a numerical study are also presented.     

Novel scaling behavior of directed polymers: Disorder distribution with long tails

We study the problem of directed polymers (DP) on a square lattice. The distribution of disorder is assumed to be independent but non-Gaussian. We show that for distributions with a power-law tailP() 1/||¹⁺ , where>2, so that the mean and variance are well defined, the scaling exponentv of the DP model depends on in a continuous fashion.