The use of the Principal components and Partial least squares methods to estimate the parameters of the logistic regression model in the case of linear multiplication problem
DOI:
https://doi.org/10.33095/jeas.v24i106.27Keywords:
الانحدار اللوجستي, البيانات الثنائية, المركبات الرئيسية, المربعات الصغرى الجزئية, مشكلة التعدد الخطي, Logistic regression, binary data, Principal components, Partial least square, multicollinearity.Abstract
Abstract
The logistic regression model is one of the nonlinear models that aims at obtaining highly efficient capabilities, It also the researcher an idea of the effect of the explanatory variable on the binary response variable.
The large number of explanatory variables usually used to illustrate the response led to the problem of linear multiplicity between the explanatory variables that make estimating the parameters of the model not very accurate.
In this paper, examined methods for estimating the parameters of the logistic regression model in the case of the problem of linear multiplicity These methods are: Principal components of logistic regression method and Partial least square regression method.
The results of the simulation showed that the method (PCLR(3pc’s)) is best for estimating the parameters of the binary logistic regression model response in the case of a problem of linear multiplicity.
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