用多级判别技术预测害虫种群动态

判别分析有基于Buyes准则和Fisher准则两种类型,它是多元统计分析中的一种方法,常常应用于气象的预测预报工作,在害虫预测预报领域中还很少见到这方面的报道。作者应用四川省营山县1971～1983年的虫情和气候资料[1],根据Buyes准则的多级判别原理,对第二代二化螟Chilosupressalis（Walker）发生量进行了分析,旨在为水稻孕期保护提供依据。1多级判别的基本原理1．1多级判别的基本思想[2]从已经掌握的历史上每个类别附体）的若干组数据（个体）出发,总结出分类的规律性（判别函数或判别方程或判别模型）。这样,对于新的个体,由巴总…     

判别水体污染的一个线性模式

本文应用判别分析的原理,根据Fisher判别准则,采用检测资料比较全面的金沙江(渡口市段)枯水期的水质资料,建立了“污染值”F与水体指标之间的数学模式,并进行了回代检验,取得了理想的效果。一、数学原理假设选用三个判别污染等级的指标X_1,X_2,X_3,并且确定判别函数为线性函数。     

判别分析方法在鉴别C_3 、C_4植物中的应用——以中国东北样带 (NECT)的研究为例

判别分析是多元统计分析中判断个体所属类型的一种重要方法。以中国东北样带(NECT)作为研究平台,利用判别分析鉴别植物光合功能型。采用国际上先进的植物光合测定系统LCA4便携式光合仪和CID_203便携式叶面积仪在野外所测定的植物生理生态参数,选取51个来自C3功能群的植物种和15个来自C4功能群的植物种构建判别模型,进行光合碳同化途径的判别。用马氏距离和后验概率判别准则进行回判,准确率达到98.48%。利用此判别模型可以根据任一植物的光合速率、蒸腾速率、气孔导度和叶温与大气温度之差4项指标判别该植物的光合碳同化功能型     

中国汉族成人髌骨性别判别初步研究

对256副中国汉族成人左右侧髌骨分别观测,显示各项指标测量值的平均值都是男性大于女性,并具有显著性差异;各指标以髌骨体积判别性别效果最好,单项判别率达94％以上;运用Fisher线性两类判别分析、逐步判别分析方法得出多项指标判别性别的公式,判别率最高达96.1％。本研究还设计了髌骨体积及髌骨内外关节面高指标的测量方法。本研究为髌骨性别判别提供了简单、实用、科学、判别率较高的新方法。     

中国汉城成人髌骨性别判别初步研究

对２５６副是国汉族成人左右侧髌骨分别观测。显示各项指标测量值的平均值都是男性大于女性,并具有显著性差异,各指标以髌骨体积判别性别效果最好,单项判别率达９４％以上,运用Ｆｉｓｈｅｒ线性两类判别分析,逐步判别分析方法得出多项指标判别性别的公式,判别率最高达９６．１％,本研究还设计了髌骨体积及髌骨内外关节面高指标的测量方法,本研究为髌骨性别判别提供了简单,实用,科学,判别率较高的新方法。     

生态系统健康评价方法在退化梭梭群落分析中的应用

健康序列评价指标的确定是健康序列分析的关键.本文运用模糊判别方法对民勤沙区梭梭群落的健康状况进行了判别分析,建立的退化植物群落健康评价指标体系包括了植物的状态指标、病态指标、多样性指标和自然更新能力指标等4个方面,分析结果较好地反映了不同群落的健康状况,且反映了优势种植物和伴生植物与群落健康序列的关系.一般来说,对于多个研究对象的健康状况进行比较排序要比只对一个研究对象的健康状况做出评价容易得多.对于荒漠植物群落应该分别乔木、灌木和草本植物进行一级判别和二级判别,对二级判别则必须进行灵敏度分析.     

上肢长骨的性别判别分析研究

本文对100例(男女各50)国人上肢骨进行了36项测量。统计结果显示所有测量项目均值男性都大于女性并具有显著性的差异。本研究表明可以采用单一指标对破损严重的肢骨进行性别鉴定。36个测量项目中有23项单一指标性别判别率达75％以上,其中9项在80％以上。本文采用Fisher判别分析法建立了56项单一肢骨性别判别函数,判别率为80％—87％。为进一步提高判别效果,采用逐步判别分析法建立了四项逐步性别判别函数,判别率达90％以上。     

一类中立型Hopfield神经网络的全局吸引集

讨论了中立型Hopfield神经网络模型,利用矩阵谱的性质和微分不等式分析等技巧,给出了其不变集和全局吸引集的判别准则．特别地,当系统有平衡点时,我们也得到了平衡点全局稳定的判别条件．     

单离子通道电信号的依赖性

运用时间序列关于信号依赖性的基本理论,讨论了单离子通道电信号的依赖性,得到关于依赖性的简明判别准则．     

半定规划鲁棒不可行性研究

本文给出了一种判别半定规划鲁棒不可行的准则,通过该准则我们可判断模型对输入数据是否敏感,进而决定是否应该利用鲁棒优化建模的方法对模型进行重新建模,使其所求解具有鲁棒性.避免了所给模型在实际应用中存在较大的风险性,为更好的应用鲁棒优化建模方法提供了理论依据.     

A new red cell discriminant incorporating volume dispersion for differentiating iron deficiency anemia from thalassemia minor

A novel red cell discriminant function [MCV2 x RDW/(Hgb x 100)] was compared to six other discriminants in 102 patients with established mild iron deficiency anemia and 33 patients with beta-thalassemia minor. The discriminant incorporates the two key measurements of erythrocyte cell volume distribution, namely the mean (MCV) and standard deviation (RDW), which are known to be helpful for distinguishing between these two frequent causes of microcytic hypochromic anemia. Data used for the learning set to develop the new discriminant were obtained using an electrical impedance automated whole blood analyzer (Coulter S + IV) and were applied as a validation set for six other discriminants. The discriminant was also tested on smaller subsets of the patients groups using data obtained on either an alternate electrical impedance instrument (Sysmex E-5000) or a laser light scattering based system (Technicon H*1). From the comparison it was concluded that use of a discriminant function that incorporates a measurement of red cell volume dispersion results in enhanced accuracy for distinguishing iron deficiency anemia from thalassemia minor.     

Two-Group Classification when Both Groups are Mixtures of Normals

In this paper we consider a two-group discriminant analysis problem where each group is a mixture of two subgroups. Based upon data from a clinical study of alcohol involvement and diseases, simulation experiments were performed for three different configurations of means and covariance matrices. Expected actual non-error rates are estimated for the linear, quadratic, and kernel discriminant functions for sample sizes 30, 50, 75, 100, 150 and 200. A conclusion of the article is that the kernel discriminant function performs as well as or better than quadratic discriminant function. However, the linear discriminant function was clearly inferior to either the quadratic or kernel discriminant functions.     

Discriminant analysis. 2. A new index for antitumor activity

The antitumor activities of 17 nitrogen analogs of 1, 4-benzoquinone, expressed in eight different ways, were subjected to discriminant analysis. Different methods of measuring the effectiveness of antitumor agents give different results. Methods based on dose levels such as the optimum dose or the lethal dose appear to be more useful in classifying compounds as antitumor agents than methods based on the maximum antitumor effect. A new index for antitumor activity involving the maximum antitumor effect, the optimum dose, and the therapeutic index, examined by discriminant analysis with substitutive parameters as discriminatory variables, can give valuable information for the design of new antitumor drugs.     

On sparse Fisher discriminant method for microarray data analysis

One of the applications of the discriminant analysis on microarray data is to classify patient and normal samples based on gene expression values. The analysis is especially important in medical trials and diagnosis of cancer subtypes. The main contribution of this paper is to propose a simple Fisher-type discriminant method on gene selection in microarray data. In the new algorithm, we calculate a weight for each gene and use the weight values as an indicator to identify the subsets of relevant genes that categorize patient and normal samples. A l(2) - l(1) norm minimization method is implemented to the discriminant process to automatically compute the weights of all genes in the samples. The experiments on two microarray data sets have shown that the new algorithm can generate classification results as good as other classification methods, and effectively determine relevant genes for classification purpose. In this study, we demonstrate the gene selection's ability and the computational effectiveness of the proposed algorithm. Experimental results are given to illustrate the usefulness of the proposed model.     

Relating new information to a previous vegetation classification: a case of discriminant coordinates analysis

Discriminant coordinates analysis is an adequate technique for analyzing the linear relationships between a number of new variates (i.e. environmental or functional attributes) and a set of vegetational attributes already summarized in the form of a classification. It displays the principal differences among classes in relation to the new variates considered. The procedure and its rationale are equivalent to a special case of principal components analysis.A case study on radiometer satellite data is presented. Two discriminant coordinates displayed the main differences in the seasonal dynamics of the NDVI (an index of standing green biomass) among broad phytogeo-graphic units in the Patagonia region. The first coordinate can be interpreted as an index of height and convexity of the NDVI seasonal curve. It suggests that the principal difference among regions was the total seasonal growth. The second coordinate represents a contrast that discriminated between two already detected patterns of seasonal NDVI curve.Abbreviations DC Discriminant Coordinate - NDVI Normalized Difference Vegetation Index     

Sex determination using the femur in an ancient Japanese population

Determination of sex from the femur measurements has been attempted in several populations and various studies have demonstrated the importance of population specific standards in the metric assessment of sex. The present study attempts to establish metric standards for sex determination by using femur measurements for ancient Japanese populations. Osteometric data were obtained from 151 adult skeletal remains from Jomon period, Yoshigo human skeletal collection. Eight femur measurements were taken and the data were analyzed by discriminant analysis using SPSS version 10.0. For the univariate discriminant function derived, precision of sex determination was 93% with the condyle breadth. Prediction values showed that sex differentiation could be done by femur measurements with reliability between 66.9 and 100%, with values for males higher than for females. It is suggested that discriminant formulas developed by femur measurements in this study, can be used for sex determination accurately on fragmentary skeletal remains in ancient Japanese populations.     

Contribution of physical fitness component to health status in middle-aged and elderly females

This study determined the physical fitness component that contributes to improving and maintaining health status for each age group as well as quantifying the degree of the relationship between health status and physical fitness in middle-aged and elderly females. The participants were 2,371 females aged 30 to 69 years. Ten physical fitness tests and medical checkups were performed. The participants were divided into a healthy group and an unhealthy group according to health status. Multiple discriminant analysis was applied to the multivariate data. Correct discriminant probabilities of the multiple discriminant function to discriminate the healthy and unhealthy groups for females ranged from 63.0% to 77.5%. These results suggest that there is a relatively high relationship between health status and physical fitness level for middle-aged and elderly females. With each individual's discriminant score calculated by the obtained multiple discriminant function as the index of the degree of health, the Pearson's correlation coefficient of the discriminant score and the performance in each physical fitness test were calculated. The aging change from 30 to 69 years old was classified into four patterns according to the contribution. The result of this study is considered to be useful as objective data to prepare an exercise program considering the contribution of the physical fitness component of health status.     

Contribution of physical fitness component to health status in middle-aged and elderly males

This study determined the physical fitness component that contributes to improving and maintaining health status for each age group as well as quantifying the degree of the relationship between health status and physical fitness in middle-aged and elderly males. The participants were 995 males aged 30 to 69 years. Ten physical fitness tests and medical checkups were performed. The participants were divided into a healthy group and an unhealthy group according to health status. Multiple discriminant analysis was applied to the multivariate data. Correct discriminant probabilities of the multiple discriminant function to discriminate the healthy and unhealthy groups for males ranged from 67.0% to 75.1%. These findings suggest that there is a relatively high relationship between the health status and physical fitness level for middle-aged and elderly males. With each individual's discriminant score calculated using the multiple discriminant function as the index of the degree of health, the Pearson's correlation coefficient of the discriminant score and the performance in each physical fitness test were calculated. The age change between 30 and 69 years old was classified into five patterns according to the contribution. The result of this study is considered to be useful as objective data to prepare an exercise program considering the contribution of the physical fitness component of health status.     

Shrinkage‐based Diagonal Discriminant Analysis and Its Applications in High‐Dimensional Data

Summary High‐dimensional data such as microarrays have brought us new statistical challenges. For example, using a large number of genes to classify samples based on a small number of microarrays remains a difficult problem. Diagonal discriminant analysis, support vector machines, and k‐nearest neighbor have been suggested as among the best methods for small sample size situations, but none was found to be superior to others. In this article, we propose an improved diagonal discriminant approach through shrinkage and regularization of the variances. The performance of our new approach along with the existing methods is studied through simulations and applications to real data. These studies show that the proposed shrinkage‐based and regularization diagonal discriminant methods have lower misclassification rates than existing methods in many cases.