TITLE:
An Application of Bootstrapping in Logistic Regression Model
AUTHORS:
Isaac Akpor Adjei, Rezaul Karim
KEYWORDS:
Nonparametric Bootstrap, Parametric Bootstrap, Logistic Regression, Confidence Interval, Test of Hypothesis
JOURNAL NAME:
Open Access Library Journal,
Vol.3 No.9,
September
28,
2016
ABSTRACT: 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.