Logistic回归的主成分改进方法探讨及其医学应用

研究Logistic回归模型的主成分回归改进方法。在医学研究中,尤其是在流行病的发病因素分析中,应用主成分回归进行多重共线性的改进处理,可以削减自变量观察矩阵之间的多重共线性,建立较为理想的关系模型,提高结果的可靠性;便于医学研究者正确合理的建立Logistic回归模型,处理混杂因素,预测疾病和判别分类。

The Study on the Enhancing Methods of Principal Component of Logistic Regression and Their Application to Medicine

This article mainly studies the enhancing methods of the principal component of Logistic Re- gression. In medical research, especially in the analyses of the aetiological agents of opidemics, the applica- tion of principal component regression to the improvements o{ multi-eollinearity can reduce the multi-col- linearity between matrices of variables , build up relatively ideal relation models and improve the reliability of results. Meanwhile, it enables medical researchers to establish rational Logistic Regression models, deal with thorny problems, predict diseases and elassify them.