Bootstrapping: A nonparametric approach to identify the effect of sparsity of data in the binary regression models

In this research, the bootstrap methods are used to investigate the effects of sparsity of the data for the binary regression models. The artificial data was created by the bootstrapping vector. We also used the percentile confidence intervals as a tool for inference, because they combine point estimation and hypothesis testing in a single inferential statement of great intuitive appeal. We found that the bootstrap confidence intervals are shorter than classical confidence intervals with the same confidence coefficient. We also found that some parameters that are non-significant when using classical confidence interval become significant with the bootstrapping sampling methods and vice versa. Moreover the bootstrap confidence intervals provided robust results for the sparse data. We also found that the sparsity of data results in the bad behaviour of the tail of the bootstrap sampling distribution, but reduction of confidence coefficient results to obtained robust confidence interval. © 2008 Asian Network for Scientific Information.