Unconstrained parametrizations for variance-covariance matrices

The estimation of variance-covariance matrices through optimization of an objective function, such as a log-likelihood function, is usually a difficult numerical problem. Since the estimates should be positive semi-definite matrices, we must use constrained optimization, or employ a parametrization that enforces this condition. We describe here five different parametrizations for variance-covariance matrices that ensure positive definiteness, thus leaving the estimation problem unconstrained. We compare the parametrizations based on their computational efficiency and statistical interpretability. The results described here are particularly useful in maximum likelihood and restricted maximum likelihood estimation in linear and non-linear mixed-effects models, but are also applicable to other areas of statistics.     

ESTIMATION OF THE CONCENTRATION PARAMETERS OF THE FISHER MATRIX DISTRIBUTION ON 50(3) AND THE BINGHAM DISTRIBUTION ON Sq, q≥ 2

Two procedures are considered for estimating the concentration parameters of the Fisher matrix distribution for rotations or orientations in three dimensions. The first is maximum likelihood. The use of a convenient 1-dimensional integral representation of the normalising constant, which greatly simplifies the computation, is suggested. The second approach exploits the equivalence of the Fisher distribution for rotations in three dimensions, and the Bingham distribution for axes in four dimensions. We describe a pseudo likelihood procedure which works for the Bingham distribution in any dimension. This alternative approach does not require numerical integration. Results on the asymptotic efficiency of the pseudo likelihood estimator relative to the maximum likelihood estimator are given, and the two estimators are compared in the analysis of a well-known vectorcardiography dataset.     

模糊亚相似矩阵和模糊相似矩阵

初步研究了模糊亚相似矩阵和模糊相似矩阵,并给出了它们的一些应用.     

MAXIMUM LIKELIHOOD ESTIMATION FOR A POISSON RATE PARAMETER WITH MISCLASSIFIED COUNTS

This paper proposes a Poisson‐based model that uses both error‐free data and error‐prone data subject to misclassification in the form of false‐negative and false‐positive counts. It derives maximum likelihood estimators (MLEs) for the Poisson rate parameter and the two misclassification parameters — the false‐negative parameter and the false‐positive parameter. It also derives expressions for the information matrix and the asymptotic variances of the MLE for the rate parameter, the MLE for the false‐positive parameter, and the MLE for the false‐negative parameter. Using these expressions the paper analyses the value of the fallible data. It studies characteristics of the new double‐sampling rate estimator via a simulation experiment and applies the new MLE estimators and confidence intervals to a real dataset.     

等参单元在货车转向架强度计算中的应用

推导了八节点六面体等参单元的应变矩阵、刚度矩阵,对该单元的位移解收敛性和畸变敏感性进行了分析。针对转向架强度分析的特点,在MATLAB平台上开发了一套求解位移的有限元程序。该程序通过读取ANSYS软件输出的数据文件中的模型信息,采用八节点六面体等参单元,求出单元刚度矩阵及单元体积力矩阵,组集单元,引入位移边界条件,得到一组以节点位移为未知量的多元线性方程组,求解得到单元节点位移。最后对160 km/h货车转向架侧架的主要载荷进行了位移计算,结果与ANSYS软件计算结果基本吻合。     

广义对角占优矩阵的充分条件

研究具有广泛应用背景的广义对角占优矩阵的充分条件，获得了几个简捷的实用结果。所得条件允许矩阵的非对角占优行可达ｎ－１行，运用于广泛的矩阵类。     

C—代数的特征矩阵的性质

本主讨论了C-代数的特征矩阵的性质,并获得了求特征矩阵的逆的一个方法。     

复正规矩阵的亚正定性

研究了复正规矩阵的亚正定性，给出了复矩阵之积为复亚正定矩阵的一系列充要条件，获得了一些新的结果；改进并推广了Ky Fan Taussky定理、Fejer定理等。     

传感器个数及距离对盲分离影响的分析

在使用盲分离技术进行信号处理时,分离高频信号和低频信号对传感器分布的要求是不同的,而传统的方法在处理高低频混合信号时采用了统一的模式,得不到很好的分离效果。为了在采用盲分离技术进行高低频混合信号处理时能得到更好的效果,文章对传感器个数和距离对盲信号分离的影响进行了深入的分析,并提出了一种能有效处理高频和低频混合信号的模型——分离子系统模型。该模型可以根据频段对传感器距离进行调整,分别对高、低频混合信号进行分离。通过实验证明分离效果更好。     

逻辑方阵的普适性探讨

2 0世纪 90年代以来 ,逻辑学界对逻辑方阵的研究取得一定成果 ,打破了逻辑方阵仅适用于简单命题范围之内的狭隘认识。认为逻辑方阵适用于一切命题。但仍显不足。笔者以为 :逻辑方阵不仅适用于命题之间 ,还适用于推理与推理之间、命题与推理之间 ,具有真正意义上的普适性特征。