Finding a single set of estimates for the parameters in a statistical model is not enough. An assessment of the uncertainty in these estimates is also needed. Standard errors and confidence intervals are common methods for expressing uncertainty. In the past, it was sometimes difficult, if not impossible, to assess uncertainty, especially for complex models. Fortunately, the speed of modern computers, and the innovations in statistical methodology inspired by this speed, have largely overcome this problem. In this chapter we apply a computer simulation technique called the “bootstrap” or “resampling” to find standard errors and confidence intervals. The bootstrap method is very widely applicable and will be used extensively in the remainder of this book. The bootstrap is one way that modern computing has revolutionized statistics.
- Good, P. I. (2005) Resampling Methods: A Practical Guide to Data Analysis, 3rd ed., Birkhauser, Boston.Google Scholar