几类矩阵的研究及其应用

对几类矩阵进行了刻划；对于非负阵谱半径的一个重要性质￣［１］，给出了新的简单证明；作为应用，得到有重要实际意义的某些类矩阵之逆的谱半径的界的估计。     

一类边值问题临界解的扰动性质

在LP(1 p <+∞ ) 空间中证明了一类非齐次积一秩微分方程边值问题的临界解关于方程系数和区域尺寸的连续依赖性 ,并获得了相应的高阶扰动公式     

NEAR-ALGEBRA和BANACH代数上的一个特征值定理(英文)

在near-algebra或Banach代数中引入(p,q)-可加自映象f和正则可逆元的概念,得到一个值得注意的结果,即在一定条件下,对于定义在near-algebra或Banach代数X中(p,q)-可加自映象f,X中的任意正则可逆元都具有公共的特征值λ=2q/(1 q),p=q≠?1。     

一类两个相同部件并联的可修系统的稳定性

研究一类两个相同部件并联的可修系统．使用泛函分析方法，特别是Banach空间上的线性算子理论，证明了严格占优本征值的存在性，以及系统解的渐近稳定性，线性稳定性和指数稳定性．     

矩阵秩的下界估计与Schur不等式的改进

研究具有实用价值的关于一般复方阵的非奇准则、秩的下界实用估计,特征值实部和虚部的平方和上界估计,所得结果改进了著名的 Schur 不等式和 Ky Fan-Hoffman 不等式的估计。     

Minkowski 不等式的进一步拓广

将正定（实对称）阵的Ｍｉｎｋｏｗｓｋｉ不等式拓广到更一般的一类矩阵，推广和改进了有关文献的结果．     

具单一休假的M/M/1排队模型的指数稳定性

研究具单一休假的M/M/1排队模型,使用泛函分析方法,特别是Banach空间上线性算子理论,证明了相应的动力算子的严格占优本征值的存在性,以及系统解的线性稳定性和指数稳定性.     

A further note on a theorem on the difference of the generalized inverses of two nonnegative definite matrices

Andrews and Phillips (1986) gave a simplified proof for the result that established the nonnegative definiteness of the difference of the Moore-Penrose inverses of two nonoegative definite matrices, a result originally due to Milliken and Akdeniz (1977), The purpose of this paper is to offer a simple proof for a generalization of this result,     

亚正定阵上的Minkowski不等式

对亚正定阵证明了一个Ｍｉｎｋｏｗｓｋｉ不等式，从而推广改进了一些文献的结果．     

Inference in model-based cluster analysis

A new approach to cluster analysis has been introduced based on parsimonious geometric modelling of the within-group covariance matrices in a mixture of multivariate normal distributions, using hierarchical agglomeration and iterative relocation. It works well and is widely used via the MCLUST software available in S-PLUS and StatLib. However, it has several limitations: there is no assessment of the uncertainty about the classification, the partition can be suboptimal, parameter estimates are biased, the shape matrix has to be specified by the user, prior group probabilities are assumed to be equal, the method for choosing the number of groups is based on a crude approximation, and no formal way of choosing between the various possible models is included. Here, we propose a new approach which overcomes all these difficulties. It consists of exact Bayesian inference via Gibbs sampling, and the calculation of Bayes factors (for choosing the model and the number of groups) from the output using the Laplace–Metropolis estimator. It works well in several real and simulated examples.