论模糊数学在假设检验中的应用

文章通过对隶属函数的集中度系数确定的探讨,将模糊数学应用于贝叶斯统计学,从而形成了一种新的假设检验方法.     

The ubiquitous angle

Summary.  Previously we used the geometry of n -dimensional space to derive the paired samples t -test and its p -value. In the present paper we describe the 'ubiquitous' application of these results to single degree of freedom linear model hypothesis tests. As examples, we derive the p - and t -values for the independent samples t -test, for testing a contrast in an analysis of variance and for testing the slope in a simple linear regression analysis. An angle θ in n -dimensional space is again pivotal in the development of the ideas. The relationships between p , t , θ , F and the correlation coefficient are also described by using a 'statistical triangle'.     

区域产业投入优化的熵权系数模型研究

针对区域产业投入的优化问题,不仅考虑到投入的经济效益,同时也考虑了投入的社会效益和生态效益,综合主观赋权法和客观赋权法,建立熵权系数模型,以确定各产业的最优投入,并举例说明模型的应用.     

网絡协議TCP/IP

网络协议TCP/IP,分别相当于ISO/OSI参考模式中的第四层和第三层,源于美国国防部ARPANET的协议标准.目前,在高档微机工程工作站中得到广泛的应用,并成为工业界的一个“公认”标准. 本文介绍TCP/IP协议,并结合对B-NET(运行于16位微机DUAL系统83和UNIX系统V)中的TCP/IP的提取和分析,着重说明它们的接口及其执行过程机理.     

Dependent masking and system life data analysis: Bayesian inference for two-component systems

Data from field operations of a system is often used to estimate the reliability of components. Under ideal circumstances, this system field data contains the time to failure along with information on the exact component responsible for the system failure. However, in many cases, the exact component causing the failure of the system cannot be identified, and is considered to be masked. Previously developed models for estimation of component reliability from masked system life data have been based upon the assumption that masking occurs independently of the true cause of system failure. In this paper we develop a Bayesian methodology for estimating component reliabilities from masked system life data when the probability of masking is dependent upon the true cause of system failure. The Bayesian approach is illustrated for the case of a two-component system of exponentially distributed components.     

DETERMINATION OF DOMAINS OF ATTRACTION BASED ON A SEQUENCE OF MAXIMA

Suppose that the maximum of a random sample from a distribution F(x) may be obtained in each of k equally spaced observation periods. This paper proposes a test to determine the domain of attraction of F(x), and investigates the properties when the sample size is very large and perhaps unknown and k is fixed and small. The test statistic is a function of the spacings between the order statistics based on the sequence of maxima and is suggested by reference to one studied previously when inference was based on the largest k observations of a random sample. A Monte Carlo study shows that the proposed test is more powerful than its main competitor. The test is illustrated by two examples.     

再入等离子体鞘套中电磁波Helmholtz方程的近似解

本文以样条函数(spline function)作为电磁波Helmholtz方程的近似解,求解了等离子体鞘套的反射系数和透射系数。本文采用的样条函数法具有数学推导和数字计算简单,可适用于任意电子浓度分布的优点,并且计算结果与美国RAM C系列飞行实测数据吻合得比较好。同时,本方法还可用于求解其它不均匀介质中的电波传播问题。     

Adaptive Gaussian Markov random fields with applications in human brain mapping

Summary.  Functional magnetic resonance imaging has become a standard technology in human brain mapping. Analyses of the massive spatiotemporal functional magnetic resonance imaging data sets often focus on parametric or non-parametric modelling of the temporal component, whereas spatial smoothing is based on Gaussian kernels or random fields. A weakness of Gaussian spatial smoothing is underestimation of activation peaks or blurring of high curvature transitions between activated and non-activated regions of the brain. To improve spatial adaptivity, we introduce a class of inhomogeneous Markov random fields with stochastic interaction weights in a space-varying coefficient model. For given weights, the random field is conditionally Gaussian, but marginally it is non-Gaussian. Fully Bayesian inference, including estimation of weights and variance parameters, can be carried out through efficient Markov chain Monte Carlo simulation. Although motivated by the analysis of functional magnetic resonance imaging data, the methodological development is general and can also be used for spatial smoothing and regression analysis of areal data on irregular lattices. An application to stylized artificial data and to real functional magnetic resonance imaging data from a visual stimulation experiment demonstrates the performance of our approach in comparison with Gaussian and robustified non-Gaussian Markov random-field models.     

变系数线性大系统稳定性的M-判别法(英文)

本文沿用文［1］的思想方法，以M矩阵为工具，研究变系数线性大系统零解的稳定性，得到一系列稳定性的显式代数判据，我们统称为M－判别法。