Comparison of Binary Logistic Regression Models Based on Bootstrap Method: An Application on Coronary Artery Disease Data

Abstract

This study is aimed to obtain an appropriate logistic regression model based on the bootstrap methods. For this purpose, two bootstrap methods called bootstrap I and bootstrap II are given to obtain the estimations of parameters and standard errors. Traditional logistic regression is compared with the bootstrap I and bootstrap II methods in terms of the parameter estimations and standard errors. It has been found that the standard errors of the parameter estimations for the bootstrap I model are smaller than others. Also, the average widths of confidence interval based on bootstrap I model are narrower than the logistic regression and bootstrap II. It is seen that, the simulation study based on different sample sizes supports these results. It can be said that the bootstrap I model based on resampling of errors term is the best in estimating coronary artery disease.

Keywords

• Bootstrap,Backward Elimination,Coronary Artery Disease,Logistic Regression,Parameter Estimation,Backward Elimination,Coronary Artery Disease,Error Term,Logistic Regression,Parameter Estimation

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