多元时间序列模型协方差矩阵的递归算法

本文推导了多元时序模型的协方差矩阵与模型参数的关系式，并给出了计算多维时序过程自协方差矩阵的递归算法     

多元时间序列模型协方差矩阵的递归算法

本推导了多元时序横型的协方差矩阵与模型参数的关系式，并给出了计算多维时序过程自协方差矩阵的递归算法。     

基于主成分分析的单变量时间序列聚类方法

针对时间序列数据的高维特性,在进行理论分析的基础上,利用主成分分析法提出了一种单变量时间序列数据降维的新方法,进而提出了基于主成分分析的单变量时间序列聚类方法。其主要思想是在线性空间中的同一组基下,用系数之间的相似性来刻画对应时间序列之间相似性,在理论分析过程中,首先对单变量时间序列数据集进行主成分分析,其次分析了单变量时间序列数据集、样本协方差矩阵的特征向量与主成分之间的关系,并证明了由主成分构成的向量组线性无关。为了进一步验证理论分析结果的正确性和所提算法的有效性,分别利用仿真数据和真实的股票数据进行了数值实验。     

具有一般协方差结构线性模型的局部影响评价

该文讨论了具有一般协方差结构线性模型的局部影响分析问题. 通过对广义Cook统计量中M/c的适当选取, 文章给出了一种对扰动的参数变换具有不变性质的局部影响度量. 在具协方差扰动模式下, 该文给出了回归系数和方差系数估计、最佳线性预测的局部影响诊断统计量.该结果与数据删除法进行了比较, 并通过实例进行了分析和说明.     

外P集合与F-扩充数据-过滤

P-集合具有动态特性,它由内P-集合与外P-集合组成.植物品种扩充过滤过程具有外-动态特性,应用外P-集合的外-动态特性及其结构,提出了F-扩充数据概念,讨论F-扩充数据的生成与过滤,获得F-扩充数据-过滤定理及F-扩充数据的生成-过滤算法,并给出F-扩充数据-过滤的应用.研究结果为动态信息系统的数据扩充提供了一种新方法.     

基于模糊分类的多维定性数据的统计分析

在多维定性数据处理的问题中,不仅要研究变量之间的关系、样本之间的关系,还需研究样本与变量之间的关系,从而对潜在样本群子结构水平及其类型特征进行分析。本文采用对偶标度统计方法利用设计矩阵变换将原始多维定性数据进行类型特征分析,并在此基础上建立了基于模糊分类的直方图,表现出了潜在样本群的特征及其子结构之间的差异与联系。文章以分析儿童主体品质发展规律为例,说明此方法的有效性。     

数据空间结构性及在KNN算法中的应用

数据空间结构性是多维数据客观存在的本征特征,是数据挖掘的重要内容.通过统计学的方法,分析多维数据空间属性变量和类型变量之间的结构特征,可以准确刻画数据在多维变量空间的相关性及其各向异性.数据空间结构特征可以用于机器学习算法的改进和提升,以提高模式识别的效果.融合了数据空间结构特征的KNN算法在稳定性和识别精度上均优于传统算法.通过在苏里格气田苏东41-33区块复杂碳酸盐岩的岩性识别中的应用表明,与传统KNN算法相比,数据空间结构的引入能提高识别准确率12.35%,并显示出算法的灵活性和适用性.多维数据空间结构的研究对机器学习算法的泛化能力和迁移性的提升等方面具有促进作用.     

基于线性回归的协方差分析模型与检验

基于虚拟变量和局部显著性检验,在线性回归的体系下重建了协方差分析理论,包括描述模型、估计参数、解释方差分解的不同形式及几何意义,得到了协方差分析的三个重要检验.还验证了响应变量校正的效果,证明了原数据中因素显著性与校正后数据中因素显著性之间的两种联系.     

一种区间数的因子分析技术及其在证券市场中的应用

传统的因子分析技术能够有效地对高维变量空间进行降维处理,但它对于样本空间却缺乏行之有效的降维效果.为了解决这一问题,一种针对大量样本数据、新的因子分析技术———区间数因子分析技术(intervaldatafactoranalysis,IFA)被提出并得到了迅速的发展。IFA方法对传统的数据概念做了本质性的扩张,运用'数据打包'的理念,对海量原始数据在不破坏其原有内在逻辑关系的前提下,可以进行变量和样本点维度的双重降维。本文详细阐述了区间数因子分析技术的原理,并以中国股票市场为案例研究背景,结果表明IFA分析技术对大规模多维数据系统做综合简化是十分有效的。     

分层混合效应模型迭代广义最小二乘估计的大样本性质

在许多领域中,我们常常需要处理具有分层结构的数据.对于这类数据,分层混合效应模型通过对回归系数进一步建模来刻画出同一层内变量之间的相关性.模型中随机部分比较复杂,这使得协方差矩阵的估计方法成为大家关注的问题.Goldstein(1986)提出了迭代广义最小二乘估计,并将它应用于一类特殊的分层模型——方差成分模型中,本文对其进行推广,对更一般的分层混合效应模型给出迭代广义最小二乘的具体表达形式,并运用到经济实例的分析中.     

Bayesian Variable Selection for Logistic Models Using Auxiliary Mixture Sampling

This article presents a Markov chain Monte Carlo algorithm for both variable and covariance selection in the context of logistic mixed effects models. This algorithm allows us to sample solely from standard densities with no additional tuning. We apply a stochastic search variable approach to select explanatory variables as well as to determine the structure of the random effects covariance matrix.

Prior determination of explanatory variables and random effects is not a prerequisite because the definite structure is chosen in a data-driven manner in the course of the modeling procedure. To illustrate the method, we give two bank data examples.     

Universality results for the largest eigenvalues of some sample covariance matrix ensembles

For sample covariance matrices with i.i.d. entries with sub-Gaussian tails, when both the number of samples and the number of variables become large and the ratio approaches one, it is a well-known result of Soshnikov that the limiting distribution of the largest eigenvalue is same that of Gaussian samples. In this paper, we extend this result to two cases. The first case is when the ratio approaches an arbitrary finite value. The second case is when the ratio becomes infinite or arbitrarily small.     

Sample covariance shrinkage for high dimensional dependent data

For high dimensional data sets the sample covariance matrix is usually unbiased but noisy if the sample is not large enough. Shrinking the sample covariance towards a constrained, low dimensional estimator can be used to mitigate the sample variability. By doing so, we introduce bias, but reduce variance. In this paper, we give details on feasible optimal shrinkage allowing for time series dependent observations.     

LLN for Quadratic Forms of Long Memory Time Series and Its Applications in Random Matrix Theory

We obtain a weak law of large numbers for quadratic forms of a stationary regular time series, imposing no rate of convergence to zero of its covariance function. We show how this law can be applied in proving universality properties of limiting spectral distributions of sample covariance matrices. In particular, we give another derivation of a recent result of Merlevède and Peligrad, who studied sample covariance matrices generated by IID samples of long memory time series.     

Testing equality between several populations covariance operators

In many situations, when dealing with several populations, equality of the covariance operators is assumed. An important issue is to study whether this assumption holds before making other inferences. In this paper, we develop a test for comparing covariance operators of several functional data samples. The proposed test is based on the Hilbert–Schmidt norm of the difference between estimated covariance operators. In particular, when dealing with two populations, the test statistic is just the squared norm of the difference between the two covariance operators estimators. The asymptotic behaviour of the test statistic under both the null hypothesis and local alternatives is obtained. The computation of the quantiles of the null asymptotic distribution is not feasible in practice. To overcome this problem, a bootstrap procedure is considered. The performance of the test statistic for small sample sizes is illustrated through a Monte Carlo study and on a real data set.     

Asymptotic expansions of the distributions of estimators in canonical correlation analysis under nonnormality

Asymptotic expansions of the distributions of typical estimators in canonical correlation analysis under nonnormality are obtained. The expansions include the Edgeworth expansions up to order O(1/n) for the parameter estimators standardized by the population standard errors, and the corresponding expansion by Hall's method with variable transformation. The expansions for the Studentized estimators are also given using the Cornish-Fisher expansion and Hall's method. The parameter estimators are dealt with in the context of estimation for the covariance structure in canonical correlation analysis. The distributions of the associated statistics (the structure of the canonical variables, the scaled log likelihood ratio and Rozeboom's between-set correlation) are also expanded. The robustness of the normal-theory asymptotic variances of the sample canonical correlations and associated statistics are shown when a latent variable model holds. Simulations are performed to see the accuracy of the asymptotic results in finite samples.     

An approximate test for common principal component subspaces in two groups

Principal component analysis has made an important contribution to data reduction. In two sample problems, one great interest is whether we can reduce the number of variables to a smaller number in similar fashions for both samples. More precisely, we consider the hypothesisH _m that the subspaces spanned by the latent vectors of the population covariance matrices corresponding to the first principal components are the same in two groups. In this paper, we propose a simple and easily interpreted test procedure forH _m .     

A Class of Robust Principal Component Vectors

This paper is concerned with a study of robust estimation in principal component analysis. A class of robust estimators which are characterized as eigenvectors of weighted sample covariance matrices is proposed, where the weight functions recursively depend on the eigenvectors themselves. Also, a feasible algorithm based on iterative reweighting of the covariance matrices is suggested for obtaining these estimators in practice. Statistical properties of the proposed estimators are investigated in terms of sensitivity to outliers and relative efficiency via their influence functions, which are derived with the help of Stein's lemma. We give a simple condition on the weight functions which ensures robustness of the estimators. The class includes, as a typical example, a method by the self-organizing rule in the neural computation. A numerical experiment is conducted to confirm a rapid convergence of the suggested algorithm.     

Asymptotic Analysis of the Jittering Kernel Density Estimator

Jittering estimators are nonparametric function estimators for mixed data. They extend arbitrary estimators from the continuous setting by adding random noise to discrete variables. We give an in-depth analysis of the jittering kernel density estimator, which reveals several appealing properties. The estimator is strongly consistent, asymptotically normal, and unbiased for discrete variables. It converges at minimax-optimal rates, which are established as a by-product of our analysis. To understand the effect of adding noise, we further study its asymptotic efficiency and finite sample bias in the univariate discrete case. Simulations show that the estimator is competitive on finite samples. The analysis suggests that similar properties can be expected for other jittering estimators.