一类二次规划问题的矩阵解法

本文给出二次规划问题的矩阵解法，经过规定的初等变换后，矩阵便同时展示出最优解判据、最优值及最优解集．     

最优消除顺序的B＆B算法

讨论了最优节点消除顺序的性质，并给出了计算最优消除顺序的Ｂ＆Ｂ算法．     

自由Fisher信息量与合并自由

在算子值非交换概率空间中引入算子值自由Fisher信息量的概念,这一定义是对D.Voiculescu在有迹的von Neumann代数上定义的自由Fisher信息量的推广．证明了算子值自由Fisher信息量与合并自由性是密切相关的,即证明了若干个算子值随机变量的自由Fisher信息量的可加性等价于这些随机变量的合并自由性．并且也类似地得到了Cramer-Rao不等式．     

广义Eady模型的非线性稳定性

对于广义Eady模型，分别讨论了密度函数是常数函数与指数函数两种情形，利用变分原理，考虑到动量守恒的约束条件，得到了优化的Poincare不等式，从而得到了新的非线性稳定性定理，并且得到了在径向长度分别不大于纬向长度的0．84402倍及0．86068倍时（这对于地球的实际情况是成立的），非线性稳定性判据与线性稳定性判据是一致的．     

线性规划的支撑方法（一）

本文简要介绍了求解线性规划的支撑方法。它引入了类似于基的支撑概念，但对非基变量不作为零的要求，即迭代不一定在极点上进行。文中给出了包括支撑可行解、ε－最优解（次优解）在内的主要概念，论述了最优性判据和次优性判据，建立了迭代算法并证明了有限步终止性。     

关于变系数线性方程的稳定性

本文给出了变系数线性方程有关稳定性的一些简洁的判据．对周期系数线性方程，给出了较为精确的渐近稳定性判据．从理论上解释了原先“冻结系数法”一般不能成立的原因．     

广义指数产品寿命试验的统计分析与优化设计

在一般逐步I型区间截尾情形下,研究广义指数产品寿命试验的统计分析与优化设计问题。基于极大似然理论,利用EM算法给出参数的极大似然估计(MLEs)及可靠性指标的统计推断。依据缺损信息原则计算Fisher信息阵,据此确定最优截尾方案。采用不同的方案对估计结果进行模拟比较,从而得出受试产品的最优分组数及最优观测时刻。最后,应用算例验证方法的有效性。     

股票债券市场中一般的随机规划问题

本文讨论了股票债券市场中一类具有停时的随机规划问题，给出了投资者在股票债券市场中的最优投资消费决策和投资消费的最优停止时刻的计算方法．     

基于LMI的时滞细胞神经网络的全局渐近稳定性分析

研究了一类具有时滞的细胞神经网络的稳定性问题,利用Liapunov-Krasovskii泛函的方法,给出了时滞相关的稳定性判据．稳定性判据是以线性矩阵不等式(LMI)的形式给出,可以很容易得出时滞的上界．在得到时滞相关的稳定性判据的同时也可以得到时滞无关的稳定性判据,包含了已有文章中的很多结果．最后,数值算例说明了结果的优越性．     

高阶线性微分方程的解的定性状态

本文的目的是考查高阶线性微分方程解的定性状态，建立方程分类的某些条件．我们还给出了方程解的振动判据．     

New results on multi-dimensional linear discriminant analysis

Fisher linear discriminant analysis is a well-known technique for dimensionality reduction and classification. The method was first formulated in 1936 by Fisher. In this paper we concentrate on three different formulations of the multi-dimensional problem. We provide a mathematical explanation why two of the formulations are equivalent and prove that this equivalency can be extended to a broader class of objective functions. The second contribution is a rate of convergence of a fixed point method for solving the third model.     

PCA-Fisher判别模型在煤层底板突水预测中的应用

针对当前煤层底板突水影响因素复杂、预测精度低及难度大等问题,通过结合主成分分析法(PCA)和Fisher判别分析法,构建了PCA-Fisher煤层底板突水判别模型,并将该判别模型应用于贵州省六盘水月亮田煤矿9号煤层对其进行底板突水危险性预测.笔者将含水层水压、隔水层厚度及煤层倾角等6个指标作为影响该煤层底板突水危险性的主要因素,把18组实测数据输入PCA-Fisher判别模型并进行煤层底板突水预测.结果显示:PCA提取的3个主成分F1、F2及F3的方差贡献率达94.179%,且判别模型的前14组训练样本正确率达85.7%;最后判别未参加训练的后4组样本,误判率为0%,其精度高达100%,结果印证了PCA-Fisher的判别模型对煤层底板突水预测的正确性.     

Discriminant analysis for compositional data and robust parameter estimation

Compositional data, i.e. data including only relative information, need to be transformed prior to applying the standard discriminant analysis methods that are designed for the Euclidean space. Here it is investigated for linear, quadratic, and Fisher discriminant analysis, which of the transformations lead to invariance of the resulting discriminant rules. Moreover, it is shown that for robust parameter estimation not only an appropriate transformation, but also affine equivariant estimators of location and covariance are needed. An example and simulated data demonstrate the effects of working in an inappropriate space for discriminant analysis.     

基于R软件的Bayes判别分析在医学中的应用

Bayes判别在进行判别分析时考虑到各总体出现的先验概率、预报的先验概率及错判造成的损失,其判别效能优于其他判别方法.对Bayes判别方法详细介绍的基础上,利用R软件对一组舒张压和胆固醇数据分别进行Bayes判别分析、Fisher判别分析和基于距离的判别分析,对比三种不同方法下得到的判别结果,结果表明Bayes判别分析得到的分类结果精度较高,Bayes判别分析在医学领域有较好的应用前景.     

军校学员的心理健康分析

本文使用分层聚类和Fisher判别等统计学方法对军校学员的心理健康状况进行统计分析。根据我校军人健康中心收集到的测查数据 ,分析了聚为不同类的学员的在心理健康方面所存在的差异与联系 ,得到了判别函数 ,为对不同学员进行针对性的心理健康教育提供了科学依据     

Learning the optimal kernel for Fisher discriminant analysis via second order cone programming

Kernel Fisher discriminant analysis (KFDA) is a popular classification technique which requires the user to predefine an appropriate kernel. Since the performance of KFDA depends on the choice of the kernel, the problem of kernel selection becomes very important. In this paper we treat the kernel selection problem as an optimization problem over the convex set of finitely many basic kernels, and formulate it as a second order cone programming (SOCP) problem. This formulation seems to be promising because the resulting SOCP can be efficiently solved by employing interior point methods. The efficacy of the optimal kernel, selected from a given convex set of basic kernels, is demonstrated on UCI machine learning benchmark datasets.     

Projection-pursuit approach to robust linear discriminant analysis

Discriminant analysis plays an important role in multivariate statistics as a prediction and classification method. It has been successfully applied in many fields of work and research. As it happens with other multivariate methods, discriminant analysis is highly vulnerable to the presence of outliers that commonly occur in many real world data sets. The lack of robustness of the classical estimators on which the linear discriminant function is based is a severe disadvantage and several authors have worked to find efficient ways to prevent the damage that outliers can cause. This paper focuses on the projection-pursuit approach to discriminant analysis. The projection-pursuit estimators are described and theoretical properties are deduced and their relevance is highlighted. These include Fisher consistency, affine equivariance, partial influence functions and asymptotic distributions. Application to real data and a simulation study reveal the robustness of the projection-pursuit approach. In both analyses the data relates to a large number of variables, a situation that is becoming common when new technology is applied to data gathering.     

Sliced Coordinate Analysis for Effective Dimension Reduction and Nonlinear Extensions

Sliced inverse regression (SIR) is an important method for reducing the dimensionality of input variables. Its goal is to estimate the effective dimension reduction directions. In classification settings, SIR is closely related to Fisher discriminant analysis. Motivated by reproducing kernel theory, we propose a notion of nonlinear effective dimension reduction and develop a nonlinear extension of SIR called kernel SIR (KSIR). Both SIR and KSIR are based on principal component analysis. Alternatively, based on principal coordinate analysis, we propose the dual versions of SIR and KSIR, which we refer to as sliced coordinate analysis (SCA) and kernel sliced coordinate analysis (KSCA), respectively. In the classification setting, we also call them discriminant coordinate analysis and kernel discriminant coordinate analysis. The computational complexities of SIR and KSIR rely on the dimensionality of the input vector and the number of input vectors, respectively, while those of SCA and KSCA both rely on the number of slices in the output. Thus, SCA and KSCA are very efficient dimension reduction methods.     

Optimal discriminant functions for normal populations

A class of discriminant rules which includes Fisher’s linear discriminant function and the likelihood ratio criterion is defined. Using asymptotic expansions of the distributions of the discriminant functions in this class, we derive a formula for cut-off points which satisfy some conditions on misclassification probabilities, and derive the optimal rules for some criteria. Some numerical experiments are carried out to examine the performance of the optimal rules for finite numbers of samples.     

Adapting the Warmack–Gonzalez algorithm to handle discrete data

Soltysik and Yarnold propose, as a method for two-group multivariate optimal discriminant analysis (MultiODA), selecting a linear discriminant function based on an algorithm by Warmack and Gonzalez. An important assumption underlying the Warmack–Gonzalez algorithm is likely to be violated when the data in the discriminant training samples are discrete, and in particular when they are nominal, causing the algorithm to fail. We offer modest changes to the algorithm that overcome this limitation.