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

Year 2019, Volume: 32 Issue: 1, 318 - 331, 01.03.2019

Hayriye Esra Akyuz , Hamza Gamgam

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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