ABSTRACT
This chapter addresses general procedures for performing statistical inference on a parameter using an estimator with minimal assumptions about its distribution. In contrast to fully nonparametric approaches used earlier, these techniques use information contained in the sample to make inferences about the sampling distribution of the estimator. The distribution of the estimator, and particularly how this distribution depends on the quantity to be estimated, is also required for statistical inference. The bootstrap is a suite of tools for inference on a model parameter, while using the data to give information that might otherwise come from assumptions about the parametric shape of its distribution. The bootstrap techniques described so far, except for the percentile method, presume that parameter values over the entire real line are possible. A similar approach may be taken to a confidence interval for the standard deviation of nail arsenic values.
