关于动力学理論中的扰动方法

本文讨论了动力学理论中求解Vlasov-Maxwell方程的扰动方法,证明了第一次线性化后的高阶分布函数对研究电磁波与带电粒子之间的互作用没有影响,并给出了第二次线性化后的Vlasov-Maxwell方程的一般求解方法.     

Accurate ARL computation for EWMA-S<Superscript>2</Superscript> control charts

Originally, the exponentially weighted moving average (EWMA) control chart was developed for detecting changes in the process mean. The average run length (ARL) became the most popular performance measure for schemes with this objective. When monitoring the mean of independent and normally distributed observations the ARL can be determined with high precision. Nowadays, EWMA control charts are also used for monitoring the variance. Charts based on the sample variance S² are an appropriate choice. The usage of ARL evaluation techniques known from mean monitoring charts, however, is difficult. The most accurate method—solving a Fredholm integral equation with the Nyström method—fails due to an improper kernel in the case of chi-squared distributions. Here, we exploit the collocation method and the product Nyström method. These methods are compared to Markov chain based approaches. We see that collocation leads to higher accuracy than currently established methods.     

高能重离子碰撞末态分布的探讨

应用流体动力学模型,分别采用包含相变的状态方程和不含相变的状态方程,计算了每对核子能量为100GeV~(197)Au—~(197)Au 碰撞的末态碎裂时空分布、末态快度分布,并分析讨论了影响末态分布的因素.     

一类推广的Halanay不等式的稳定性研究及其应用

利用Halanay不等式的推广形式,证明了一类时滞微分系统的指数稳定性,得到了一个新的积分条件,推广和改进了已有的结果,并结合例子加以说明.     

三阶中立型分布时滞阻尼微分方程的振动定理

研究一类三阶非线性中立型阻尼泛函微分方程,利用Riccati变换和积分平均技巧,建立了保证该类方程的一切解振动或者收敛于零的若干新的充分条件,推广和改进最近文献的结果.     

The Beta Product Distribution with Complex Parameters

The family consisting of the distributions of products of two independent beta variables is extended to include cases where some of the parameters are not positive but negative or complex. This “beta product” distribution is expressible as a Meijer G function. An example (from risk theory) where such a distribution arises is given: an infinite sum of products of independent random variables is shown to have a distribution that is the product convolution of a complex-parameter beta product and an independent exponential. The distribution of the infinite sum is a new explicit solution of the stochastic equation X = (in law) B(X + C). Characterizations of some G distributions are also proved.     

Theory of Bilinear Time Series Models

ABSTRACT

In this paper, we prove some theoretic properties of bilinear time series models which are extension of ARMA models. The sufficient conditions for asymptotic stationarity and ivertibility of some types of bilinear models are derived. The structural theory of discussed bilinear models is similar to that of ARMA models. For illustration, a bilinear model has been fitted to the Wolfer sunspot numbers and a substantial reduction in sum of squared residuals is obtained as comparing with Box-Jenkins ARMA model.     

A detection algorithm for the first jump time in sample trajectories of jump-diffusions driven by α-stable white noise

Abstract

The purpose of this paper is to develop a detection algorithm for the first jump point in sampling trajectories of jump-diffusions which are described as solutions of stochastic differential equations driven by α-stable white noise. This is done by a multivariate Lagrange interpolation approach. To this end, we utilize computer simulation algorithm in MATLAB to visualize the sampling trajectories of the jump-diffusions for various combinations of parameters arising in the modeling structure of stochastic differential equations.     

Cointegrated systems II

The three invited papers in this special issue of Econometric Reviews on "Cointegrated Systems II" complement the previous special issue of the journal. The paper by Eric Zivot and Peter Phillips provides a comprehensive Bayesian analysis of trend determination in economic time series. Two interesting comments on some aspects of current research involving cointegration and the modelling of dynamic economic relationships are provided by Clive Granger and Denzil Fiebig.     

Exact Likelihood Equations for Autoregression Models with Multivariate Elliptically Contoured Distributions

Abstract

The multivariate elliptically contoured distributions provide a viable framework for modeling time-series data. It includes the multivariate normal, power exponential, t, and Cauchy distributions as special cases. For multivariate elliptically contoured autoregressive models, we derive the exact likelihood equations for the model parameters. They are closely related to the Yule-Walker equations and involve simple function of the data. The maximum likelihood estimators are obtained by alternately solving two linear systems and illustrated using the simulation data.