Bootstrap 101: obtain robust confidence intervals for any statistic
page Paper 193 - 29. (2004)3755<m:linebreak></m:linebreak>Resampling.Abstract
For almost any statistic of interest, SAS/STAT PROCs generally contain options for obtaining a confidence interval. Some PROCs even provide multiple computational methods for estimating the standard errors and confidence intervals. In almost every case, however, the accuracy of the confidence intervals depends on parametric assumptions. In such cases, bootstrap methods may be used to obtain a more robust non-parametric estimate of the confidence intervals. Bootstrap samples are very easy to generate using SAS software; however, it is a very computationally intensive method. In particular, the method is easy to apply in its most basic form even if you are not already familiar with bootstrap methods, as long as you are not already stretching the capabilities of your CPU and disk space. The rationale for the bootstrap and the basics for interpreting the confidence intervals are explained through an example. The most efficient way to program and compute bootstrap confidence intervals depends in part on the size of the data set and the power of oneÆs computer. Two different approaches are suggested depending on the limitations of ones data set and computing environment.