基于Copula函数和非参数估计的尾部相关性

给出基于Copula函数的尾部相关性的定义和性质,采用非参数方法估计尾部相关系数.结合数据得出上证指数和深圳指数的尾部相关系数和对应图形比较,可知两种股票的上尾比下尾相关性强.此相关系数反映了上证指数与深圳指数在极端值处同时小于或同时大于某个数值的概率大小.     

成分数据相关系数的计算方法

成分数据具有非常复杂的数学性质,很多传统的统计分析方法对其是失效的,因此,在研究中必须采用特殊处理和专门技术.着重讨论了成分数据相关系数的计算方法,由于普通数据的相关系数计算方法只适用于两组单变量数据,而传统的典型相关分析又鉴于成分数据的特殊性质而不能直接使用,故结合logratio变换和典型相关分析技术,提出了一种针对成分数据的相关系数计算方法,成功地解决了这一问题.     

相关系数的传递性

主要研究了相关系数的传递性.首先在区间[-1,1]上引入两个运算和⊕,并讨论了它们的性质.接着利用运算和⊕给出了相关系数的传递性:当Xi与Xk完全相关,Xk与Xj完全相关时,Xi与Xj也完全相关.     

关于D-相关系数的若干推广

在[1]文中,张尧庭利用了多样性指标的性质,给出了离散随机变量的一类相关性度量(D-相关系数).本文利用了信息论的方法,给出了关于非离散随机变量 D-相关系数的若干推户,这些新的相关系数进一步反映了随机变量(或随机过程)的相关性特征.     

相关系数矩阵与多元线性相关分析

相关系数指度量两个随机变量间线性关系的无量纲指标,在研究了相关系数矩阵性质及其与多元随机变量线性相关性之间关系的基础上,提出多元线性相关系数的定义,用于衡量多个变鼋间线性相关强弱的无量纲指标.分析表明,所提多元线性相关系数能够较全面地反映变量间的线性相关强度.     

独立非同分布二元高斯三角阵最大值的渐近性及相关统计推断

考虑二元独立非同分布高斯随机向量三角阵列最大值分布的渐近性及相关统计推断.此高斯三角阵的第n列的第i个向量服从二元高斯分布,其相关系数为i/n的函数并单调连续.首先建立了此高斯三角阵最大值分布的一阶和二阶渐近展开式.其次,分析相关系数参数估计及估计量的渐近性质.最后,通过随机模拟说明了相关系数之参数估计的有效性,并将该二元非同分布三角阵列模型应用于实际数据,得到了满意的结果.     

论带有趋势变化的变量的相关:数值试验

当计算相关的二个变量都包含有明显的趋势变化成分时,原变量之间的相关特征可能被歪曲(夸大或者缩小).对此问题进行了数值试验,结果表明,变量带有性质相反的趋势变化,会使这二个变量之间的相关系数减小(正相关的数值减小,负相关被夸大).变量带有性质相同的趋势变化,会使这二个变量之间的相关系数增加(正相关被夸大,负相关数值变小).数值试验还表明,趋势变化对相关的影响具有可交换性.只要不改变它们趋势变化的数值,它们叠加的变量互相交换,影响相关系数的后果是一样的;研究还指出,二个变量有相同的变化趋势时,对相关的影响会更大些.给出了实例.     

股票收益率尾部相关性的Copula度量及模拟

股票收益率尾部相关性是研究金融市场关联性的重要内容.由于传统的τ、ρ等相关系数是对随机变量的全局度量,不适合用于收益率分布尾部这种局部特征的相关性度量.因此,在引入左尾(右尾)相关系数的基础上,讨论了它们的Copula度量及其相关性质.最后,通过计算机模拟分析了沪、深股指收益率尾部相关性的变化趋势,有效避免了Copula模型的设定困难,并得到了尾部相关性增强、相关不对称等结论.     

关于相关系数的一些不等式

相关系数是概率统计中的一个重要概念,其绝对值的大小反应了两个随机变量线性关联程度的高低.我们考虑以下两个问题,通过一些简单的不等式以期对其性质有更深入的了解.     

泛逻辑学中UB代数系统的广义fuzzy滤子

将区间值fuzzy集的概念应用于理想状态(广义相关系数h=0.5,广义自相关系数k=0.5)下泛逻辑学所对应的代数系统--UB代数,引入区间值(∈,∈Vq)-fuzzy滤子和区间值(∈,∈Vq)-fuzzy关联滤子的概念并研究它们的性质.获得了UB代数的这两类广义fuzzy滤子的若干等价刻画,证明了区间值(∈,∈Vq)-fuzzy关联滤子的扩张定理.     

相关系数概念剖析

从两个角度——内积空间以及线性回归角度深入剖析了相关系数这一重要概念,将其与R~2空间中向量之间的夹角联系起来,并且给出了一种迅速判断随机变量之间相关性强弱的方法,并通过随机模拟进行了直观展示.     

Time aggregation effect on the correlation coefficient: added-systematically sampled framework

The aggregation of financial and economic time series occurs in a number of ways. Temporal aggregation or systematic sampling is the commonly used approach. In this paper, we investigate the time interval effect of multiple regression models in which the variables are additive or systematically sampled. The correlation coefficient changes with the selected time interval when one is additive and the other is systematically sampled. It is shown that the squared correlation coefficient decreases monotonically as the differencing interval increases, approaching zero in the limit. When two random variables are both added or systematically sampled, the correlation coefficient is invariant with time and equal to the one-period values. We find that the partial regression and correlation coefficients between two additive or systematically sampled variables approach one-period values as n increases. When one of the variables is systematically sampled, they will approach zero in the limit. The time interval for the association analyses between variables is not selected arbitrarily or the statistical results are likely affected.     

On Robust estimation of a correlation coefficient

Robust estimation of the correlation coefficient of a bivariate normal distribution is considered in the case of a contamination scheme. A number of conventional robust estimates are studied, and some new estimates are proposed. Their properties are examined on finite samples and in asymptotics with the use of Monte-Carlo and the influence functions techniques correspondingly. It is shown that one of the proposed estimates called a median correlation coefficient has high robustness properties. Proceedings of the XVII Seminar on Stability Problems for Stochastic Models. Kazan, Russian, 1995, Part II.     

相关系数与相关性度量

研究了度量相关性的两个主要工具:线性相关系数和尾部相关系数.线性相关系数反映了变量间的线性相关性,这对于一般的椭圆型分布是合适的.但如果随机变量具有不对称的尾部变化特征时,要用尾部相关系数描述它们之间的相关性.通过相关函数C opu la,对沪深股市的尾部相关系数进行了定量分析.结果表明:沪深股市具有较强的相关性.     

The covariance of uncertain variables: definition and calculation formulae

Uncertainty theory as a branch of axiomatic mathematics has been widely used to deal with human uncertainty. The two commonly used numerical characteristics of uncertain variables, the expected value and the variance together with their mathematical properties have been discussed and applied to real optimization problems in an uncertain environment. As a further study, in this paper, we focus on the covariance and correlation coefficient of uncertain variables. The definitions and calculation formulae of covariance and correlation coefficient of two uncertain variables are suggested by means of their inverse distributions. Then we show that the correlation coefficient of uncertain variables is essentially a measure of the relevance of distributions of uncertain variables. Finally, the relation between variance and covariance is analysed and represented with some equalities and inequalities.     

Priority estimation in the AHP through maximization of correlation coefficient

This paper proposes a correlation coefficient maximization approach (CCMA) for estimating priorities from a pairwise comparison matrix. The priorities are supposed to be as highly correlated with each column of a pairwise comparison matrix as possible. Such priorities are not unique and can be determined in different ways. Two optimization models are therefore suggested for determining further the priorities, one of which leads to an analytic solution. Theorems about the CCMA are developed and its potential applications are illustrated with two numerical examples.     

A class of multivariate symmetric stable distributions

Multivariate symmetric stable characteristic functions and their properties, as well as conditions for independence and an analogue of the correlation coefficient in bivariate symmetric stable distributions, are discussed.     

关于相关系数的探讨

讨论统计学中的线性相关系数和非线性相关系数,寻找其共性.对比研究与信息再利用.得到一个相关系数的通用公式.该公式适合于统计学中的各种数据处理.     

Measuring monotony in two-dimensional samples

This note introduces a monotony coefficient as a new measure of the monotone dependence in a two-dimensional sample. Some properties of this measure are derived. In particular, it is shown that the absolute value of the monotony coefficient for a two-dimensional sample is between |r| and 1, where r is the Pearson's correlation coefficient for the sample; that the monotony coefficient equals 1 for any monotone increasing sample and equals ?1 for any monotone decreasing sample. This article contains a few examples demonstrating that the monotony coefficient is a more accurate measure of the degree of monotone dependence for a non-linear relationship than the Pearson's, Spearman's and Kendall's correlation coefficients. The monotony coefficient is a tool that can be applied to samples in order to find dependencies between random variables; it is especially useful in finding couples of dependent variables in a big dataset of many variables. Undergraduate students in mathematics and science would benefit from learning and applying this measure of monotone dependence.