追加数据对条件指数的影响

克服线性回归模型中设计矩阵的复共线性，主要有三种方法追加数据自变量选择和非量小二乘法，本文研究追加数据在减少条件数中的作用，我们的研究表明，在可能情况下适当地选择追加数据，设计矩阵的条件指数可以被减少。我们用在回归最优设计中广泛初研Ｇａｙｌｏｒ－Ｍｅｒｒｉｌｌ数据说明了本文理论结果的实用意义。     

稳健矩阵回归模型和方法研究

随着大数据时代的到来,我们面临的数据越来越复杂,其中待估系数为矩阵的模型亟待构造和求解.无论在统计还是优化领域,许多专家学者都致力于矩阵模型的统计性质分析及寻找其最优解的算法设计.当随机误差期望为0且同方差时,采用基于最小二乘的模型可以很好地解决问题.当随机误差异方差,分布为重尾分布(如双指数分布,t-分布等)或数据含有异常值时,需要考虑稳健的方法来求解问题.常用的稳健方法有最小一乘,分位数,Huber等.目前稳健方法的研究大多集中于线性回归问题,对于矩阵回归问题的研究比较缺乏.本文从最小二乘模型讲起,对矩阵回归问题进行了总结和评述,同时列出了一些文献和简要介绍了我们的近期的部分工作.最后对于稳健矩阵回归,我们提出了一些展望和设想.     

含高维相依自变量的中心k阶条件矩子空间的估计

在回归分析中往往对条件均值,条件方差及高阶条件矩特别感兴趣.本文我们将关注中心k阶条件矩子空间在高维相依自变量情形的估计问题.为此,我们首先引入中心k阶条件矩子空间的概念,并研究该子空间的基本性质.针对高维相依自变量的复杂数据,为了避免预测变量协方差阵的逆矩阵的计算,本文提出用偏最小二乘方法来估计中心k阶条件矩子空间....     

回归模型中中心化刻度变换对复线性度量的影响

对线性回归模型，本文研究了中心化和刻度变换对复共线性的影响，建立了原来设计矩阵，中心化的设计矩阵和刻度变换过的设计矩阵的条件数和条件指数之间的一些关系，并给出了刻度变换过设计矩阵的条件数的下界。     

基于copula函数的纵向数据复合分位数回归及变量选择

复合分位数回归(composite quantile regression)具有稳健性好和估计效率高的优势,所以其经常被用来替代均值回归.众所周知,纵向数据具有组内相关的特点,如果估计过程中能正确地利用组内相关性,则可以显著地提高估计效率.因此,探讨纵向数据复合分位数回归中如何使用相关性是一个有意义的问题.本文首先利用copula函数方法构建纵向数据复合分位数回归的组内协方差矩阵,进而基于构建的协方差矩阵,提出一个无偏且有效的基于copula函数的复合分位数回归估计方程;进一步,为了进行变量选择,利用基于copula函数的估计方程,提出一个光滑门限(smooth-threshold)的复合分位数回归估计方程方法.本文提出的方法具有很高的灵活性,而且提高了估计的效率.理论结果以及数值模拟和实际数据分析都验证了本文的方法.     

矩阵损失下一类相依回归模型中的线性容许估计和Minimax估计

本文研究设计矩人有相同值域的相依回归模型,在矩阵损失下我们给出了回归系数的线性估计是线性容许的充要条件,它们推广了已有的结果,我们也在矩阵上给出了某个回归模型的回归系数的唯一的Ｍｉｎｉｍａｘ估计,它说明此时其它模型的信息不起作用     

Autoregression Model of Time Series with Matrix Cross-Section Data北大核心CSCD

矩阵型截面数据时间序列的优点在于可以同时刻画多个对象的多个属性.本文重点研究了矩阵型截面数据时间序列的自回归模型,给出了该模型的参数估计、模型识别、白噪声检验三个方面的理论结果.最后再利用矩阵型截面数据时间序列自回归模型,对两支银行股的日收益率序列和日成交量变化率序列进行建模分析.     

线性回归诊断(Ⅰ)

回归分析的近期发展大致可归纳为四个方面:自变量选择、有偏估计、稳健估计和回归诊断。关于前三个内容;文献[1]已作了较详细的讨论。相对说来,回归诊断发展较晚些。本文拟就共理论和应用两个方面作一介绍。 §1　引言 具有m个自变量的线性回归模型为这里, βj, j=0, 1, 2,…,m为未知参数.通常称 β1,…, βm为回归系数,e为随机误差。当我们做了n次试验或者观测之后,得到n组数据(yi,χi1,…,χim),i=1,2, n,它们满足若引进矩阵记号则上述回归模型为在回归分析中,设计矩阵X一股总是列满秩的,即秩R(X)=。+1.为方便计,我们约定n。 m+ 1. e为…     

用M方法检测回归系数矩阵的秩

为了确定多重线性回归模型中回归系数矩阵的秩，　本文提出了一个基于Ｍ估计的模型选择程序，　且在较弱的条件下建立了回归系数矩阵的秩的估计的强相合性。     

基于病例队列设计的平均处理效应的估计

病例队列设计因为具有成本效益而被广泛应用于流行病学和生物医学的研究中.对于病例队列设计,现有的统计方法主要集中在如何得到回归参数的相合及有效的估计上,然而很少有工作估计非随机化处理的因果效应.本文基于病例队列设计数据提出了一种有效的估计平均处理效应的方法,建立了所提估计量的相合性和渐近正态性,并通过仿真研究考察了其在有限样本下的表现.最后,我们将所提方法应用于真实数据的分析中.     

On condition numbers of polynomial eigenvalue problems

In this paper, we investigate condition numbers of eigenvalue problems of matrix polynomials with nonsingular leading coefficients, generalizing classical results of matrix perturbation theory. We provide a relation between the condition numbers of eigenvalues and the pseudospectral growth rate. We obtain that if a simple eigenvalue of a matrix polynomial is ill-conditioned in some respects, then it is close to be multiple, and we construct an upper bound for this distance (measured in the euclidean norm). We also derive a new expression for the condition number of a simple eigenvalue, which does not involve eigenvectors. Moreover, an Elsner-like perturbation bound for matrix polynomials is presented.     

A Note on Random Effects Growth Curve Models

The robustness of regression coefficient estimator is a hot topic in regression analysis all the while. Since the response observations are not independent, it is extraordinarily difficult to study this problem for random effects growth curve models, especially when the design matrix is non-full of rank. The paper not only gives the necessary and sufficient conditions under which the generalized least square estimate is identical to the the best linear unbiased estimate when error covariance matrix is an arbitrary positive definite matrix, but also obtains a concise condition under which the generalized least square estimate is identical to the maximum likelihood estimate when the design matrix is full or non-full of rank respectively. In addition, by using of the obtained results, we get some corollaries for the the generalized least square estimate be equal to the maximum likelihood estimate under several common error covariance matrix assumptions. Illustrative examples for the case that the design matrix is full or non-full of rank are also given.     

Riesz界与条件数

在多分辨分析与小波分析中,人们经常要估计Riesz基的上下界.在有限维空间中,这等价于计算Riesz基所对应Gramian矩阵的条件数.本文给出Riesz基与条件数的关系并且讨论了Riesz基加入元素后对Riesz界产生的影响.     

复方厚朴片生产工艺实验研究的优化方法

为优化复方厚朴片的生产工艺，先通过预实验找出六个影响因素及其范围，对各因素如何搭配采用随机办法安排实验方案，按五种优良性准则从方案中选出较优且运行次数最少者，用ＳＡＳ回归过程分析试验结果     

Condition number related to the outer inverse of a complex matrix

In this paper we obtain the formula for computing the condition number of a complex matrix, which is related to the outer generalized inverse of a given matrix. We use the Schur decomposition of a matrix. We characterize the spectral norm and the Frobenius norm of the relative condition number of the generalized inverse, and the level-2 condition number of the generalized inverse. The sensitivity for the generalized Drazin-inverse solution of linear systems is presented. We also present the structured perturbation of the generalized inverse.     

The condition numbers for weighted Moore-Penrose inverse and weighted linear least squares problem

Condition numbers play an important role in numerical analysis. Classical normise condition numbers are used to measure the size of both input perturbations and output errors. In this paper, we study the weighted normwise relative condition numbers for the weighted Moore-Penrose inverse and the weighted linear least-squares (WLS) problems in the case of the full-column rank matrix. The bounds or formulas for the weighted condition numbers are presented. The obtained results can be viewed as extensions of the earlier works studied by others.     

Book Review

A method of estimation of the coefficients of a linear regression model is described as invariant if the basic regression results obtained by the method are unaltered by a location/scale transformation of the data matrix. A necessary and sufficient condition for a method to be invariant is presented. Finally specific methods of estimation are examined for invariance.     

Further results on adaptive full-order and reduced-order observers for Lur’e differential inclusions

This paper is concerned with the adaptive observer design of Lur’e differential inclusions with unknown parameters. Under a relaxed assumption on nonlinear perturbation functions, a sufficient condition for the existence of an adaptive full-order observer is established. Comparing with results in the literature, the present conditions are complemented with a numerically reliable computational approach, which can be checked by means of linear matrix inequalities. Furthermore, it is shown that, under the sufficient condition, the existence of a reduced-order observer is guaranteed. Also, the reduced-order observer is designed. The effectiveness of the proposed design is illustrated via a simulation example.     

On the exploration of linear latent effect for multivariate modeling

A data analysis method is proposed to derive a latent structure matrix from a sample covariance matrix. The matrix can be used to explore the linear latent effect between two sets of observed variables. Procedures with which to estimate a set of dependent variables from a set of explanatory variables by using latent structure matrix are also proposed. The proposed method can assist the researchers in improving the effectiveness of the SEM models by exploring the latent structure between two sets of variables. In addition, a structure residual matrix can also be derived as a by-product of the proposed method, with which researchers can conduct experimental procedures for variables combinations and selections to build various models for hypotheses testing. These capabilities of data analysis method can improve the effectiveness of traditional SEM methods in data property characterization and models hypotheses testing. Case studies are provided to demonstrate the procedure of deriving latent structure matrix step by step, and the latent structure estimation results are quite close to the results of PLS regression. A structure coefficient index is suggested to explore the relationships among various combinations of variables and their effects on the variance of the latent structure.