AN EXPLANATION OF THE USE OF PRINCIPAL-COMPONENTS ANALYSIS TO DETECT AND CORRECT FOR MULTICOLLINEARITY
AN EXPLANATION OF THE USE OF PRINCIPAL-COMPONENTS ANALYSIS TO DETECT AND CORRECT FOR MULTICOLLINEARITY
复制标题
DOI：
10.1016/0167-5877(92)90041-d
复制
发表时间：
1992-09-01
影响因子：
2.6
通讯作者：
KANEENE, JB
中科院分区：
文献类型：
--
作者：
LAFI, SQ;KANEENE, JB
在数据分析中，多重共线性可能是一个严重的统计问题，在数据分析中，每个单独的风险因素的贡献都在评估中。讨论了在检测数据库中多重共线性的存在时有用的症状、效应和技术。论证了主成分分析技术用于检测、量化和调整数据库中多重共线性影响的回归系数的数学基础。
Multicollinearity can be a serious statistical problem in data analysis in which the contribution of each individual risk factor is being evaluated. Symptoms, effects and techniques that are useful in detecting the presence of multicollinearity in a data base are discussed. The mathematical basis of the principal-components analysis technique for detecting, quantifying, and adjusting the regression coefficients for the effects of multicollinearity in a data base was demonstrated.