共线性

9个染色体区域发现明显的偏分离，但DH图谱与RGP和CU图谱之间仍表现出较好的共线性。

Segregation distortion was observed in 9 chromosomal segments . Nevertheless , the DH map still shows good colinearity with the RGP and the CU maps .

共线性的影响是使回归系数不可靠。

The effect of colinearity is to make the regression coefficients unreliable .

GDP预测模型中的多重共线性问题

The Problems of Multiple Synteny in the Models of Predicting GDP

多因子共线性的主成分logistic回归分析

Logistic Regression Based on Principal Component Analysis in Resolving the Co-linearity

胃癌危险因素研究中多因子共线性的logistic回归分析

Multi-variable Collinearity in Logistic Regression Model : an Application to Study on the Risk Factors of Gastric Carcinoma

Cox模型多因子共线性处理方法的进一步研究

Further Study on Methods in Handling Multi-variable Collinearity in Cox Regression Model

文章将面板数据PANELDATA应用于信息资源的测度，从原理、方法、实例等方面进行详细阐述，试验证明PANELDATA能够提供更多信息、更少共线性、更多自由度和更高效率。

This paper attests that panel data can provide more information , less multicollinearity , more freedom degree and more efficiency from principles , methods , examples in information resource fields .

结论在Logistic回归模型分析中应用上述方法进行多重共线性的诊断和处理是有效及可行的。

Conclusion The new method is effective and feasible for diagnosis and treatment of multivariable multicollinearity in the logistic regression model analysis .

Grange因果关系检验、VAR模型被证明具有较好的预测效果；逐步回归则有效的克服了多重共线性带来的问题。

VAR model has good forecast effect and Stepwise regression can solve multicollinearity .

目的探讨PLS回归理论在消除多元共线性中的作用。

Objective To research the NIPALS algorithm of PLS regression in the function of eliminating collinearities .

主元分析（PCA）能够有效地提取数据的特征信息，消除变量间的共线性；

Principle component analysis ( PCA ) can extract the characteristics of input data and eliminate the collinearity of variables .

为了避免多重共线性，使用因子分析的方法将财务指标简化为6个不相关变量，建立了logistic模型进行回归分析。

Factor analysis method is utilized to simplify the financial indexes to 6 independent variables , and a logistic model is adopted for regression analysis .

由于自变量过多，且共线性问题严重，故先采取主成分分析方法，将七个影响因素划分为两个新的变量，分别命名为A和B。

Because too many independent variables , and the serious problem of linear , the seven factors are divided into two new variable named respectively A and B with principal component analysis method .

Gauss-Markov模型存在复共线性时的一般主成分回归

Generalized Principal Components Regression in the Gauss-Markov Models with Multi-collinearity

采用条件Logistic回归模型进行单因素分析，对单因素分析中有意义的预选变量进行共线性诊断后，进行主成分分析和因子分析，然后进行多因素分析。

Conditional logistic regression , principal component analysis , factor analysis and generalized relative risk model were used to analyse the risk factors and the potential interactions between some risk factors .

考虑到偏最小二乘法（PLS）在处理多重共线性数据中的优势，利用PLS对高炉铁水含硅量进行预测。

Considering the advantages of partial least square ( PLS ) in dealing with collinear data , it is used to predict silicon content .

结果表明CO2影响因素之间的复共线性可以忽略。经检验，回归方程与回归系数均有较好的显著性。

The result is that the multicollinearity between the effect element may be ignored , and the infective of the regression equation together with the regression coefficient are distinctness by significance testing .

另外，比较了两个遗传图谱间SSR标记的共线性并对定位区域进行了生物信息分析。

In addition , we do comparison study on the colinearity of SSR markers between two genetic maps and then do a bioinformatics analysis to the position area .

但当设计阵X存在复共线性时，最小二乘估计存在着明显的缺陷，线性有偏估计则是改进最小二乘估计最直接的方法。

But when the design matrix X has the problem of multicollinearity , the ordinary least squares estimation shows apparently disadvantage , the linear biased estimation is the most direct method in ameliorating the ordinary least squares estimation .

针对设计矩阵的多重共线性问题，为了改进基于最小二乘估计的统计诊断量Cook距离，提出了基于Massy主成分下的Cook距离（MPCC距离）。

In the light of the approximate multicollinearity of matrix , distance for principal components estimation ( namely distance ) is put forward .

二是面板数据中的变量有更多信息含量的数据、更多的变异性（variability）、更少的共线性（collinearity）、更多的自由度和更强的有效性。

Two is there more variables panel data information content of data , more variation variability , the less of linear collinearity , more and more freedom .

采用Frisch综合分析法，运用SAS软件包作辅助工具，解决多重共线性问题。

We adopt Frisch analytic approach of synthesizing , use SAS act as handling tool by software package and solve multiple synteny problems .

QSAR研究常采用偏最小二乘回归分析（PLS）建立模型，以解决大量分子结构描述符的使用带来的变量间多重共线性问题。

Partial least squares regression ( PLS ) is a main modeling method in QSAR studies of organic pollutants , for it can analyze data with strong multicollinearity .

然后，在回归分析之前，通过对各变量进行Person相关分析，对各变量之间的相关性有了一定了解，并发现不会产生多重共线性问题，为下一步的回归分析打下基础。

Then , before the regression analysis , we calculate Person correlation analysis among those variables . We get some ideas of the correlation of the variables and find there is not multicollinearity among those variables . This has reached a basis for the next regression analysis .

正则矩阵ATA的列向量呈强复共线性（病态）时，将影响参数最小二乘解的质量。

N case of stronger multi-colinearity among column vectors in regular matrix A TA , the quality of the least squares solution will be affected .

研究非共线性声光可调谐滤波器（AOTF）在一般相位匹配条件下的声光相互作用关系。

The optic-acoustic reaction relationship is analyzed to noncollinear acousto-optic tunable filter ( AOTF ) under normal phase matching condition in this paper .

该建模方法的优点在于：径向基函数的引入赋予岭回归方法非线性功能，同时岭回归方法又可以消除使用RBF进行非线性处理后RBF输出之间潜在的复共线性。

The advantages of this modeling method is that the introduction of the RBF gives the Ridge Regression a nonlinear ability and the RR can also eliminate latent multicollinearity after the nonlinear process using the RBF .

用回归方法作企业财务困境判别模型时，财务指标的取舍依赖t检验和F检验，保留的指标受主观假设和共线性问题影响。

In the discriminant analysis and prediction of corporate financial distress , selections of financial ratios in regression model rely on t-test and F-test , and the retained variables may contain the impact of subjective hypothesis and the problem of collinearity .

首先，利用共线性和欧氏距离不变性这两个特性可以求得图像中的点在相机坐标系（CCS）中的坐标。

First , the counterparts of the points on image in camera coordinate system ( CCS ) are found by utilizing two properties , namely collinearity and Euclidean distance invariability .

结果：实例分析表明，PLS对数据的拟合度和预测精度均优于另一个常用于处理多重共线性的统计方法：OLS逐步回归。

RESULTS : It was illustrated by this case that in goodness of fit and prediction , PLS was better than OLS stepwise regression , another statistical technique dealing with multicollinearity .