Computer intensive methods have recently been
intensively studied in the field of mathematics, statistics, physics,
engineering, behavioral and life sciences. Bootstrap is a computer intensive
method that can be used to estimate variability of estimators, estimate
probabilities and quantile related to test statistics or to construct
confidence intervals, explore the shape of distribution of estimators or test
statistics and to construct predictive distributions to show their asymptotic
behaviors. In this paper, we fitted the classical logistic regression model, and performed both parametric and non-parametric bootstrap for estimating
confidence interval of parameters for logistic model and odds ratio. We also
conducted test of hypothesis that the prevalence does not depend on age.
Conclusions from both bootstrap methods were similar to those of
classical logistic regression.
Cite this paper
Adjei, I. A. and Karim, R. (2016). An Application of Bootstrapping in Logistic Regression Model. Open Access Library Journal, 3, e3049. doi: http://dx.doi.org/10.4236/oalib.1103049.
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Midi, H. (2012) Robust
Bootstrap Methods in Logistic Regression Model. 2012 International Conference on Statistics in Science, Business, and Engineering (ICSSBE), Langkawi, 10-12
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Empirical Distributions of Parameter Estimates in Binary Logistic Regression
Using Bootstrap. International Journal of
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Strategy Guidelines. In: Logistic
Regression, Part of the Series Statistics
for Biology and Health, Springer, Berlin, 165-202. http://dx.doi.org/10.1007/978-1-4419-1742-3_6
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