Research On The Properties Of Stein Ridge Principal Component Of Estimates For Parameters In The Logistic Regression Model

Logistics regression model is an effective method to deal with classified data.It has been widely used in many fields such as economics,engineering,medicine,biology,and criminal psychology system.In recent years,more and more use of the Logistic regression model for statistical analysis,in many estimation theories and methods,maximum likelihood estimation and least squares accounted for a large proportion.However,as statisticians continue to study in depth,many biased estimates of Logistic regression models are proposed to improve the maximum likelihood estimation and least squares estimation in dealing with the problem of complex collinearity,such as ridge estimation,Liu estimation.This paper mainly compares the relationship between the ridge ridge principal component estimation and the two-parameter estimation of the Logistic regression model,and the relationship between the almost unbiased Stein ridge principal component estimation and the almost unbiased two-parameter estimation of the Logistic regression model.