Evaluating Bag Of Little Bootstraps On Logistic Regression With Unbalanced Data

Abstract: The Bag of Little Bootstraps (BLB) was introduced to make the bootstrap method more computationally efficient when used on massive data samples. Since its introduction, a broad spectrum of research on the application of the BLB has been made. However, while the BLB has shown promising results that can be used for logistic regression, these results have been for well-balanced data. There is, therefore, an obvious need for further research into how the BLB performs when the dependent variable is unbalanced and whether possible performance issues can be remedied through methods such as Firths's Penalized Maximum Likelihood Estimation (PMLE). This thesis shows that the dependent variable's imbalances severely affect the BLB's performance when applied in logistic regression. Further, this thesis also shows that PMLE produces mixed and unreliable results when used to remedy the drops in performance.