我国上市公司财务困境预测新探

本文采用主成分分析方法确定模型变量,建立多元判别分析(MDA)、Logistic回归和改进型BP神经网络模型进行财务困境预测。结果表明,神经网络模型的预测准确率明显优于多元判别分析和Logistic回归模型,而后两者的判别效果接近,神经网络模型更适合于财务困境预测。但三种模型的长期预警能力不够理想,提出建立以定量模型为主、定性分析为辅的上市公司财务困境预测新方法。     

基于混合模型的动态判别分析及应用

当检验数据中包含有新的类别时,传统判别分析方法所构造的分类器,无法识别这些新类别,只能将检验数据划分到学习阶段所遇到的已知类别当中,分类正确率较低.为克服这一缺陷,文章引入一种基于混合模型的动态判别分析方法,可自适应调整原有的分类器,使之能够发现新类别,并显著提高分类正确率.一个实际数据的分类结果验证了该方法的有效性.     

一个基于神经网络——Logistic回归的混合两阶段个人信用评分模型研究

一、问题的提出在建立个人信用评分模型时 ,预测精度是非常重要的 ,因为许多情况下即使预测的准确性只提高一点点 ,也会使信贷机构的损失减少很多。正因为如此 ,大量的统计分类技术被应用到信用评分领域。文 [1]首次利用中国某商业银行的信用卡客户数据对多种个人信用评分方法在中国的适用状况进行了全面的比较研究。结果表明 ,不同的模型有自己不同的优点和缺点 :神经网络等非线性方法的精度往往要高于 (线性 )判别分析、Logistic回归、线性规划等线性评分方法 ;而Logistic回归、判别分析、线性规划等方法的稳健性① 则比神经网络方法要好…     

我国上市公司财务危机预警模型研究

文章在分析14个变量的基础上,运用判别分析给出了判别上市公司财务危机的一个模型。通过逐步回归筛选得到4个变量。模型的回判准确率为83.2%,而对2002年的公司判别准确率达到90%。     

缺失偏态数据下联合位置与尺度模型的统计推断

为了研究缺失偏态数据下的联合位置与尺度模型,基于分布自身的特点,提出了一种适合缺失偏态数据下联合建模的插补方法———修正随机回归插补方法,该方法对缺失数据下模型偏度参数的调整十分显著。通过随机模拟和实例研究,并与回归插补和随机回归插补方法进行比较,结果表明,所提出的修正随机回归插补方法是有用和有效的。     

序次Logistic回归模型中因变量分类数的确定

当因变量为定性数据时,Logistic回归模型经常被使用,其中又以二分类因变量(取值为0或1)的Logistic模型最为常见。其实,Logistic回归模型也可以应用于多分类因变量,即因变量的分类数大于等于3的情况。而且,多分类因变量既可以是序次的(Ordinal),也可以是名义的(Nominal)。当多分类因变量类别之间有序次关系时,一般采用序次(或累积)Logistic回归模型。人们在进行此类回归分析时,往往只注重通过一定的手段选择合适的自变量,以达到预期的拟合效果,却忽视了对因变量取值的研究。由于序次Logistic回归模型其实隐含了对因变量分类的一种假设条件…     

随机右截尾保险寿命数据的非参数回归估计

文章分别运用bagged最近邻估计与kn-最近邻估计的非参数回归方法对随机右截尾假定下的保险寿命数据进行估计,分析了两种估计的收敛速度,并对两种估计的估计精度通过随机模拟方法进行比较,结果显示bagged最近邻估计优于kn-最近邻估计。     

使用评估模型的判别分析

一、回归与判别分析 所谓判别分析,就是要判别一个样品究竞属于哪一类比较合适.这样做的前提是对总体已有一个分类.为了对总体分类,一般应该有训练样本,它的分类和统计指标都是已知的,然后从训练样本计算出判别规则,再根据判别规则去判别那个样品究竟属于哪一类.判别分析主要方法有距离判别、Bayes判别、Fisher判别等.     

缺失偏态数据下线性回归模型的统计推断

研究缺失偏态数据下线性回归模型的参数估计问题,针对缺失偏态数据,为克服样本分布扭曲缺点和提高模型的回归系数、尺度参数和偏度参数的估计效果,提出了一种适合偏态数据下线性回归模型中缺失数据的修正回归插补方法.通过随机模拟和实例研究,并与均值插补、回归插补、随机回归插补方法比较,结果表明所提出的修正回归插补方法是有效可行的.     

Logistic回归模型的两参数估计

Logistic回归模型是一种有效的分类数据处理方法.为了克服Logistic回归模型的复共线性,文章提出了Logistic回归模型参数的两参数估计,并在均方误差矩阵准则下对估计的统计性质进行研究.     

一次基于平均策略的判别财务危机试验

文章基于平均策略,使用BP神经网络对贝叶斯判别、费歇尔线性判别和logistic回归判别财务危机的输出新变量进行加权平均再判别,并和单一方法判别的效果比较。应用双层分类器做了一次财务危机判别的新尝试。     

MISSING VALUES, IMPUTATION AND ERROR RATE ESTIMATORS IN DISCRIMINANT ANALYSIS

Error rate is a popular criterion for assessing the performance of an allocation rule in discriminant analysis. Training samples which involve missing values cause problems for those error rate estimators that require all variables to be observed at all data points. This paper explores imputation algorithms, their effects on, and problems of implementing them with, eight commonly used error rate estimators (three parametric and five non-parametric) in linear discriminant analysis. The results indicate that imputation should not be based on the way error rate estimators are calculated, and that imputed values may underestimate error rates.     

Logistic与分类树模型变量筛选的比较——基于信用卡邮寄业务响应率分析

基于信用卡邮寄业务响应率分析来讨论Logistic模型和分类树模型在变量选取上的区别,并尝试从几个不同角度去解释两类模型变量筛选差异的原因。笔者认为没有绝对占优势的方法,需要结合具体场景和模型的特点来选择合适的模型。分类树模型在训练集上容易过度拟合,对单个变量的影响很敏感,在进行危险因素分析时结果更能强调危险因素,对孤立点的识别率很高。Logistic模型容易受到解释变量依存关系的影响,加上分类变量的影响容易过多地选入变量或者因子,对孤立点敏感,对噪点不敏感。判别函数的差异是变量筛选差异的关键因素。     

Integrating linear discriminant analysis,polynomial basis expansion,and genetic search for two-group classification

We propose a hybrid two-group classification method that integrates linear discriminant analysis, a polynomial expansion of the basis (or variable space), and a genetic algorithm with multiple crossover operations to select variables from the expanded basis. Using new product launch data from the biochemical industry, we found that the proposed algorithm offers mean percentage decreases in the misclassification error rate of 50%, 56%, 59%, 77%, and 78% in comparison to a support vector machine, artificial neural network, quadratic discriminant analysis, linear discriminant analysis, and logistic regression, respectively. These improvements correspond to annual cost savings of $4.40–$25.73 million.     

On discrimination procedure with mixtures of continuous and categorical variables

A discrimination procedure, based on the location model is described and suggested for use in situation where the discriminating variables are mixtures of continuous and binary variables. Some procedures that have been previously employed, in a similar situation, like Fisher's linear discriminant function and the logistic regression were compared with this method using error rate (ER). Optimal ERs for these procedures are reported using real and simulated data for the case of varying sample size and number of continuous and binary variables and were used as a measure for assessing the performance of the various procedures. The suggested procedure performed considerably better in the cases considered and never did produce a result that is poor when compared with other procedures. Hence, the suggested procedure might be considered for such situations.     

Component reduction in linear discriminant analysis

The linear discriminant function is transformed into a linear combination of independent random variables. It is shown that reducing dimensionality using the smallest distance criterion results in smaller increase in the error rate than using the smallest variance criterion. Three error rates are used to prove this.     

Logistic回归模型在判别分析中的应用

介绍Logistic回归模型用于判别的方法,利用给出的某期间华北地区和长江中下游降水年变化为判别对象,以这种判别方法确定界于两个地区中间地带的一些观测站属于何种年变化型,并且与传统用的最大概率法做了比较,发现Logistic的效果要比最大概率法好。     

The efficiency of Efron's “Bootstrap” Approach Applied to Error Rate Estimation in Discriminant Analysis

The “bootstrap” approach of Efron is considered in its application to the estimation of error rates in discriminant analysis. Its efficiency relative to parametric estimation is investigated by simulation for Fisher's linear discriminant function in the context of two multivariate normal populations with a common covariance matrix.     

ON SELECTING VARIABLES AND ASSESSING THEIR PERFORMANCE IN LINEAR DISCRIMINANT ANALYSIS

Linear discriminant analysis between two populations is considered in this paper. Error rate is reviewed as a criterion for selection of variables, and a stepwise procedure is outlined that selects variables on the basis of empirical estimates of error. Problems with assessment of the selected variables are highlighted. A leave-one-out method is proposed for estimating the true error rate of the selected variables, or alternatively of the selection procedure itself. Monte Carlo simulations, of multivariate binary as well as multivariate normal data, demonstrate the feasibility of the proposed method and indicate its much greater accuracy relative to that of other available methods.     

Asymptotic relative efficiency of the linear discriminant function under partial nonrandom classification of the training data

This paper considers the problem where the linear discriminant rule is formed from training data that are only partially classified with respect to the two groups of origin. A further complication is that the data of unknown origin do not constitute an observed random sample from a mixture of the two under- lying groups. Under the assumption of a homoscedastic normal model, the overall error rate of the sample linear discriminant rule formed by maximum likelihood from the partially classified training data is derived up to and including terms of the first order in the case of univariate feature data. This first- order expansion of the sample rule so formed is used to define its asymptotic efficiency relative to the rule formed from a completely classified random training set and also to the rule formed from a completely unclassified random set.