关于正态随机变量的线性函数的分布

<正> 关于正态随机向量有结论:一个n维正态随机向量(ξ_1,ξ_2,…,ξ_n)的线性函数a_1ξ_1+a_2ξ_2+…+a_nξ_n是一维正态随机变量,其中a_i,i=1,2,…,n是不全为0的实数。n个相互独立正态随机变量是n维联合正态的,故n个独立正态随机变量之线性函数是一维正态的。     

数字时间序列分析(Ⅱ)——第一讲 随机数据分析基础(Ⅱ)

2-2.正态分布重要性质 正态分布有许多独有的重要性质,仅介绍常用到的几个 Ⅰ.独立性和相关性等价 如果一个正态分布向量的各分量之间不相关,即cov(x(i),x(j))=0,i≠j,i,j= 1,2,…,s, var x(i)=σi2,这时协方差阵R=(), x的概率密度可写成量x(i)的边缘概率密度,此式表明,对于正态随机向量,其各分量两两相互独立的充分必要条件是它们两两不相关.同样可以证明,两个有联合正态密度的随机向量相互独立的充分必要条件是它们不相关。 Ⅱ.正态分布的条件密度保持正态性不变 我们先对二维正态分布情形证明这个性质的正确性 设(x(1),x(2))服从二维正…     

基于DDC算法的稳健稀疏主成分法及其实证研究

主成分分析是多元统计分析中经典降维方法之一。它有两个固有弊端:一是当样本中存在离群样本时,经典主成分法所得载荷向量、得分往往不符合实际;二是在现实中各主成分载荷往往都会不等于零,甚至经常还会出现次要变量与主要变量的载荷绝对值大小接近的情况,导致主成分可解释性被大幅削弱。另外,传统的稳健主成分法通过删除离群样本后计算载荷向量达到稳健效果,这对于那些只有少数几个变量的观测值离群的离群样本来说是一种欠妥的方法。针对上述几点,本文以DDC (Detecting Deviating Cell)算法为主要的稳健方法,提出一种稳健稀疏主成分法DDCSPCA。模拟实验和实证分析结果表明:DDCSPCA在处理有离群样本的数据时能达到稳健与(载荷向量)稀疏双重效果。而且,其对格离群数据有着以往稳健主成分法所远远不及的稳健性。     

关于二维和三维正态随机量在任意区域内的概率的近似计算

在诸多领域中,二维和三维正态分布的随机量都有其重要价值.但是要在任意区域上计算二维和三维正态随机量的概率又十分繁难.本文利用区域变换法,对任意的二维正态随机量以及较为广泛的一类三维正态随机量在任意区域上的概率的近似计算问题进行了讨论并给出若干估计方法和近似计算公式.     

一般形式下斜正态随机向量的矩

本文给出一般形式下斜正态随机向量及其平方型的矩公式. 作为应用, 计算出了斜正态随机向量的多元偏度和峰度.     

主变量筛选方法

本文利用矩阵的扫描运算，提出一种对高维随机向量X=（x1,x2,…,xp)‘进行降维处理的实用方法-主变量筛选方法，给出了该方法的理论依据、直观解释、算法及数值例子。该方法是不同于主成分分析法的一种降维方法。特别，当变量X多重相关性突出时，本文方法效果显著。     

基于主成分分析的支持向量机模型对上海房价的预测研究

采用基于主成分分析的支持向量机方法对上海房价进行预测.首先利用主成分分析法对原始数据进行降维处理,然后利用具有高水平的小样本学习能力的支持向量机进行预测模型的建立,对上海房价进行预测.实证显示,经过主成分分析的支持向量机模型能够较好地处理复杂的房地产数据,具有较高的预测能力,为上海房地产业的发展提供参考.特别地,该模型可以普遍应用于影响因素众多,时效性较强的短期小样本数据问题的预测,具有较高的泛化能力和很好的预测精度.     

基于统计深度函数的G-Q检验

G-Q检验是一种简单、有效的异方差检验方法,但该方法只适用于一个自变量,在多变量情况下,文献[1]利用主成分对样本数据进行排序,得到了G-Q检验的推广.众所周知,主成分分析是一种有效的降维方法,但其在降维的同时伴随着信息的损失,统计深度函数可作为多元数据排序的有效工具.本文基于统计深度函数得到了推广了的G-Q检验,并应用于实例。     

自适应设计广义线性回归多维拟似然估计的渐近正态性

本文研究了自适应设计下广义线性回归的拟似然方程∑ni=1xi(yi-μ(xi′β))=0,其中yi是q维向量,xi是p×q阶随机矩阵,在一定条件下证明了方程的解^βn具有渐进正态的性质.     

二元分布函数的密度收敛

设 { ( Xi,Yi) ,i≥ 1 }是独立同分布二维随机向量列 ,其共同分布函数为 F.设 F属于 G的吸引场 ,本文假定边缘分布满足 Von-Mises条件 ,主要考虑二维极大值向量 Mn 密度收敛局部一致成立的问题 .本文将 Resnick[3 ]的结果推广到了二维情形     

Convolutions of multivariate phase-type distributions

This paper is concerned with multivariate phase-type distributions introduced by Assaf et al. (1984). We show that the sum of two independent bivariate vectors each with a bivariate phase-type distribution is again bivariate phase-type and that this is no longer true for higher dimensions. Further, we show that the distribution of the sum over different components of a vector with multivariate phase-type distribution is not necessarily multivariate phase-type either, if the dimension of the components is two or larger.     

On the residual dependence index of elliptical distributions

The residual dependence index of bivariate Gaussian distributions is determined by the correlation coefficient. This tail index is of certain statistical importance when extremes and related rare events of bivariate samples with asymptotic independent components are being modeled. In this paper we calculate the partial residual dependence indices of a multivariate elliptical random vector assuming that the associated random radius has distribution function in the Gumbel max-domain of attraction. Furthermore, we discuss the estimation of these indices when the associated random radius possesses a Weibull-tail distribution.     

Number of Records in a Bivariate Sample with Application to Missouri River Flood Data

For a sequence of observations from a bivariate absolutely continuous distribution, two types of records are considered depending on whether a univariate record is established in both or in at least one of the components. The distributional properties of the associated univariate and bivariate record indicators are examined. Correlation between the number of component records and the first two moments of the number of bivariate records in a finite random sample are obtained. These are evaluated for the Farlie-Gumbel-Morgenstern and bivariate normal distributions. Large sample properties of these moments are explored. Our results are used to predict the number of record annual floods at two sites along the Missouri river during the next 50 years.     

Detecting a conditional extreme value model

In classical extreme value theory probabilities of extreme events are estimated assuming all the components of a random vector to be in a domain of attraction of an extreme value distribution. In contrast, the conditional extreme value model assumes a domain of attraction condition on a sub-collection of the components of a multivariate random vector. This model has been studied in Heffernan and Tawn (JRSS B 66(3):497–546, 2004), Heffernan and Resnick (Ann Appl Probab 17(2):537–571, 2007), and Das and Resnick (2009). In this paper we propose three statistics which act as tools to detect this model in a bivariate set-up. In addition, the proposed statistics also help to distinguish between two forms of the limit measure that is obtained in the model.     

Preservation of certain dependent structures under bivariate homogeneous Poisson shock models

A bivariate Poisson shock model resulting from two devices receiving shocks from two independent sources is shown to preserve certain bivariate dependent structures such as total positivity of order 2 (TP₂), stochastic increasing (SI), right tail increasing (RTI) etc. However, when two devices are subjected to the same source of shocks it is observed through a counter example that some of these preservation results do not hold any more. In such cases sufficient conditions are given under which the bivariate random vector denoting the life lengths of two devices is shown to have the above-mentioned bivariate dependent structures.     

A multivariate nonparametric test of independence

A new nonparametric approach to the problem of testing the joint independence of two or more random vectors in arbitrary dimension is developed based on a measure of association determined by interpoint distances. The population independence coefficient takes values between 0 and 1, and equals zero if and only if the vectors are independent. We show that the corresponding statistic has a finite limit distribution if and only if the two random vectors are independent; thus we have a consistent test for independence. The coefficient is an increasing function of the absolute value of product moment correlation in the bivariate normal case, and coincides with the absolute value of correlation in the Bernoulli case. A simple modification of the statistic is affine invariant. The independence coefficient and the proposed statistic both have a natural extension to testing the independence of several random vectors. Empirical performance of the test is illustrated via a comparative Monte Carlo study.     

The pairwise beta distribution: A flexible parametric multivariate model for extremes

We present a new parametric model for the angular measure of a multivariate extreme value distribution. Unlike many parametric models that are limited to the bivariate case, the flexible model can describe the extremes of random vectors of dimension greater than two. The novel construction method relies on a geometric interpretation of the requirements of a valid angular measure. An advantage of this model is that its parameters directly affect the level of dependence between each pair of components of the random vector, and as such the parameters of the model are more interpretable than those of earlier parametric models for multivariate extremes. The model is applied to air quality data and simulated spatial data.     

Generation of random bivariate normal deviates and computation of related integrals

A method which transforms two random variables having rectangular distributions into a pair of bivariate normal deviates with prescribed covariance matrix is described. The same transformation is used for integrating the bivariate normal distribution over areas which are the intersection of the domain outside an equiprobability ellipse and a sector determined by two lines through the point of gravity of the normal distribution.     

Representation of Downton’s bivariate exponential random vector and its applications

Downton’s bivariate exponential distribution is one of the most important bivariate distributions in reliability theory. In this paper a simple representation for Downton’s bivariate exponential random vector is given. As an application of this representation, we consider a reliability model where an item is subject to shocks and obtain an explicit expression for the long-run cost rate.     

Large deviations of bivariate Gaussian extrema

We establish sharp tail asymptotics for componentwise extreme values of bivariate Gaussian random vectors with arbitrary correlation between the components. We consider two scaling regimes for the tail event in which we demonstrate the existence of a restricted large deviations principle and identify the unique rate function associated with these asymptotics. Our results identify when the maxima of both coordinates are typically attained by two different versus the same index, and how this depends on the correlation between the coordinates of the bivariate Gaussian random vectors. Our results complement a growing body of work on the extremes of Gaussian processes. The results are also relevant for steady-state performance and simulation analysis of networks of infinite server queues.