蠓的线性判别

本文在没有任何假设的条件下，建立了一个对蠓分类的数学模型．所用理论工具，计算工具都很简单．且经反馈检验．对已知种类的蠓模型判断完全正确．     

部分线性模型中的非参数部分的线性性检验

In this paper,we propose the test statistic to check whether the nonpara- metric function in partially linear models is linear or not.We estimate the nonpara- metric function in alternative by using the local linear method,and then estimate the parameters by the two stage method.The test statistic under the null hypothesis is calculated,and it is shown to be asymptotically normal.     

多元线性模型中两类预测的最优性判别

在一般多元线性模型中就基于岭估计的预测量与最优线性无偏预测量的最优性判别问题进行了讨论,得到了基于岭估计的预测量在矩阵迹意义下优于最优线性无偏预测量的充要条件.     

约束线性模型异常值检验

本文讨论了带约束线性模型的数据删除模型和均值漂移模型，得到了带约束情况下上述两个模型的统计量之间的关系，建立了相应的异常值检验统计量及性质。     

一类特殊的线性函数关系模型

本文从实际问题出发，首次提出了一类特殊的线性函数关系模型，并给出了模型参数的似然估计方法和一个应用例子。     

污染线性模型的非参数估计

Abstract. In this paper, the following contaminated linear model is considered:     

多元线性模型中的有偏估计

刘金山在1999年给出了多元线性模型参数分量βi和参数矩阵B的有偏估计β1=(X'X)-1YCi,i=1,…,m和B=(β1,β2,…,βm)以及βi的性质.本文从另一角度得到了同样的估计,证明了刘金山文中所给的两个检验是UMP检验,估计βi是βi的线性可容许估计,证明了B不是B的可容许估计,由此给出了两种改进估计.     

线性模型中F-检验的稳健性

本文讨论了一般线性模型中关于均值参数β的线性假设基于广义最小二乘估计的F-检验统计量的稳健性问题.主要研究了当误差的协方差矩阵含有参数时,设计阵可以列降秩情况下的F-检验统计量的稳健性,得到了F(V(θ))为该假设下F-检验统计量的误差协方差矩阵的最大类.并讨论了分块线性模型中,关于分块参数的线性假设的F-检验统计量的稳健性.     

线性调控分枝过程

本文提出了线性调控分枝过程和在零点停止的线性调控分枝过程的数学模型,并讨论了它们的均值、方差函数和灭绝概率,从而推广了[1]、[3]和[4]有关结果。     

一类线性模型中参数的经验Bayes检验问题

本文对一类线性模型构造了其参数的经验Bayes（EB）检验函数．在适当的条件下证明了所提出的EB检验函数是渐近最优的,其收敛速度可任意接近O（n-1/2）．最后讨论了EB检验问题在线性回归模型、方差分析模型和协方差分析模型中的应用。     

判别分析在组织人才测评和选拔模型开发中的应用

动用判别分析的方法,以卡特尔16种人格因素测验(16PF)测得的人格因素及其二级因素为预测变量,以员工绩效评估的等级为被预测变量,开发出用于组织人事选拔的判别函数和判别模型。用交叉验证法对模型进行的评估表明,模型具有较理想的预测精度。该模型的应用解决了人才测评指标多元化和复杂化为人事选拔带来的困难,提高了组织人事决策的效率和准确性。     

Classification Trees With Bivariate Linear Discriminant Node Models

This article introduces a classification tree algorithm that can simultaneously reduce tree size, improve class prediction, and enhance data visualization. We accomplish this by fitting a bivariate linear discriminant model to the data in each node. Standard algorithms can produce fairly large tree structures because they employ a very simple node model, wherein the entire partition associated with a node is assigned to one class. We reduce the size of our trees by letting the discriminant models share part of the data complexity. Being themselves classifiers, the discriminant models can also help to improve prediction accuracy. Finally, because the discriminant models use only two predictor variables at a time, their effects are easily visualized by means of two-dimensional plots. Our algorithm does not simply fit discriminant models to the terminal nodes of a pruned tree, as this does not reduce the size of the tree. Instead, discriminant modeling is carried out in all phases of tree growth and the misclassification costs of the node models are explicitly used to prune the tree. Our algorithm is also distinct from the “linear combination split” algorithms that partition the data space with arbitrarily oriented hyperplanes. We use axis-orthogonal splits to preserve the interpretability of the tree structures. An extensive empirical study with real datasets shows that, in general, our algorithm has better prediction power than many other tree or nontree algorithms.     

Bayes判别分析在医疗数据处理中的应用

本文利用判别分析的基本原理和方法,针对肝硬化医疗数据建立数学模型,然后利用SPSS16.0作为工具求解模型,得到了三个有意义的能判别归类的函数判别式。     

Testing Hypotheses on Discriminant Space in the Mixture Model of Gaussian Distributions

When clustering multivariate observations adhering the mixture model of Gaussian distributions, rather frequently projections of the observations onto a linear subspace of less dimensionality, called discriminant space (DS), contain all statistical information about the cluster structure of the model. In this case, the actual reduction of data dimensionality substantially facilitates a solution of various classification problems. In the paper, attention is devoted to statistical testing of hypotheses about DS and its dimension. The characterization of DS and methods of its identification are also briefly discussed.     

The Optimal Classification Using a Linear Discriminant for Two Point Classes Having Known Mean and Covariance

The current study provides a simple algorithm for finding the optimal ROC curve for a linear discriminant between two point distributions, given only information about the classes' means and covariances. The method makes no assumptions concerning the exact type of distribution and is shown to provide the best possible discrimination for any physically reasonable measure of the classification error. This very general solution is shown to specialise to results obtained in other papers which assumed multi-dimensional Gaussian distributed classes, or minimised the maximum classification error. Some numerical examples are provided which show the improvement in classification of this method over previously used methods.     

Some Properties of A Lack-of-Fit Test for a Linear Errors in Variables Model

The relationship between the linear errors-in-variables model and the corresponding ordinary linear model in statistical inference is studied. It is shown that normality of the distribution of covariate is a necessary and sufficient condition for the equivalence. Therefore, testing for lack-of-fit in linear errors-in-variables model can be converted into testing for it in the corresponding ordinary linear model under normality assumption. A test of score type is constructed and the limiting chi-squared distribution is derived under the null hypothesis.Furthermore, we discuss the power of the test and the choice of the weight function involved in the test statistic.     

Discriminant Analysis for Regression Models with Stationary Long-Memory Disturbances

We shall consider the problems of classifying an observation from regression model with stationary long-memory or short-memory disturbances into one of two populations described by the mean functions of the model. We use the log-likelihood ratio as a discrimant statistic which is optimal in the sense of its minimizing the misclassification probabilities. Then we confirm the theoretical results by some simple polynomial regression models.     

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.     

The Effect of Spatial Autocorrelation on the Error Rates of the Linear Discriminant Functions

The problem of classification of the realization of the intrinsically stationary multivariate Gaussian random field into one of two populations with different means and factorized covariance matrices is considered. Unknown means and the common covariance matrix of the feature vector components are estimated from the spatially correlated training samples assuming spatial correlations to be known. Two plugin linear discriminant functions (DF) are considered. The first linear DF uses the maximum likelihood (ML) estimators of means and the biasadjusted ML estimator of covariance. The second one uses usual sample means and biasadjusted sample covariance. The firstorder asymptotic expansions with respect to the inverses of training sample sizes of the expected error rate associated with two plugin DF's are presented. The numerical results obtained allow us to compare the performance of the suggested DF's. The numerical calculations are done for the exponential spatial correlation function.