可对称化矩阵特征值的Weyl型和Wielandt型扰动界

该文利用矩阵的奇异值分解得到了可对称化矩阵特征值的Weyl型和Wielandt型绝对扰动上界,并推广了Weyl-ЛиДский定理和Wielandt-Hoffman定理.     

The efficiency and optimality characterization of certain six-Treatment incomplete-Block designs emanating from some quasi-Semi-Latin squares

Incomplete-block designs were constructed from three types of three-factor block-structured combinatorial designs which are not necessarily semi-Latin squares but whose treatments are ab initio arranged as in the semi-Latin square. The basic consideration for the construction involves the exploitation of the different block structures of the experimental units of the Quasi-semi-Latin Squares (QSLS) for the same treatments. All the emanating designs are found to be connected, only the designs in nine blocks are both variance- and efficiency-balanced and offer gain in efficiency but lead to about 46 percent loss of information.     

Cross-validation approximation in functional linear regression

Cross-validation has been widely used in the context of statistical linear models and multivariate data analysis. Recently, technological advancements give possibility of collecting new types of data that are in the form of curves. Statistical procedures for analysing these data, which are of infinite dimension, have been provided by functional data analysis. In functional linear regression, using statistical smoothing, estimation of slope and intercept parameters is generally based on functional principal components analysis (FPCA), that allows for finite-dimensional analysis of the problem. The estimators of the slope and intercept parameters in this context, proposed by Hall and Hosseini-Nasab [On properties of functional principal components analysis, J. R. Stat. Soc. Ser. B: Stat. Methodol. 68 (2006), pp. 109–126], are based on FPCA, and depend on a smoothing parameter that can be chosen by cross-validation. The cross-validation criterion, given there, is time-consuming and hard to compute. In this work, we approximate this cross-validation criterion by such another criterion so that we can turn to a multivariate data analysis tool in some sense. Then, we evaluate its performance numerically. We also treat a real dataset, consisting of two variables; temperature and the amount of precipitation, and estimate the regression coefficients for the former variable in a model predicting the latter one.     

ASYMPTOTIC DISTRIBUTION OF THE LARGEST EIGENVALUE

ABSTRACT

This paper studies the asymptotic distribution of the largest eigenvalue of the sample covariance matrix. The multivariate distribution for the population is assumed to be elliptical with finite kurtosis 3κ. An expression as an expectation is obtained for the distribution function of the largest eigenvalue regardless of the multiplicity, m, of the population's largest eigenvalue. The asymptotic distribution function and density function are evaluated numerically for m = 2,3,4,5. The bootstrap of the average of the m largest eigenvalues is shown to be consistent for any underlying distribution with finite fourth-order cumulants.     

Fixed-Effects Modeling of Cohen's Kappa for Bivariate Multinomial Data

Cohen's kappa statistic is the conventional method that is used widely in measuring agreement between two responses when they are categorical. In this article, we develop a fixed-effects modeling of Cohen's kappa for bivariate multinomial data which reduces to Cohen's kappa under certain conditions and hence can be considered as a generalization of the conventional Cohen's kappa. Also, this method can easily be adapted as a generalization of Cohen's weighted kappa. Properties of the proposed method are provided. Large sample performance is investigated through bootstrap simulation studies followed by two illustrative examples.     

Sensitivity measures of influence on the loading matrix in exploratory factor analysis

For the detection of influential observations on the loading matrix of the factor analysis model, we propose to use the infinitesimal version of two matrix coefficients, including Escoufier (1973)'s also discussed the application in factor analysis of some sensitivity measures used for similar purposes in principal component analysis.     

状态转移矩阵的若干性质

给出了有限域上的状态转移矩阵 ,讨论了其周期、特征根与特征向量的一些性质     

Estimation of systems of equations subject to curvature constraints

Two computationally manageable estimation procedures for maintaining curvature constraints are compared. Using an efficient nonlinear optimizing algorithm, the well-known Cholesky factorization is compared with the eigenvalue decomposition method for estimating a system of input demand and output supply equations. Statistical tests are provided. The Cholesky factorization is superior when efficient optimizers are used. The eigenvalue decomposition may become a better choice when the user is limited to less efficient algorithms.     

限值响应超平面模式识别的模型及计算方法

用模式识别的观点，探讨了以本征值分解法研究响应值在窄的上下限内的限值控制问题．通过变换坐标表象（ｒｅｐｒｅｓｅｎｔａｔｉｏｎ），可以将一个超平面嵌入响应值符合控制要求的输入模式的薄分布层中，并划分出一定的模糊分界面±ｄ’，使之与响应值的限定范围±ｄ相对应．子空间中的超平面由协方差矩阵的一组本任解确定，且最小本征值所对应的本征矢为超平面的法矢．从拟合特定输入模式的分布出发，建立输入－输出之间的一般联系，减少了在模型建立和修正过程中对系统正常运行的干扰．     

用矩阵讨论多元函数的极值问题

利用矩阵给出了多元函数在稳定点取极值的充分条件,提出了解决两类极值问题的代数方法.