The jackknife and bootstrap are the most popular data-resampling meth-
ods used in statistical analysis. The resampling methods replace
theoreti- cal derivations required in applying traditional methods (such
as substitu- tion and linearization) in statistical analysis by
repeatedly resampling the original data and making inferences from the
resamples. Because of the availability of inexpensive and fast
computing, these computer-intensive methods have caught on very rapidly
in recent years and are particularly appreciated by applied
statisticians. The primary aims of this book are (1) to provide a
systematic introduction to the theory of the jackknife, the bootstrap,
and other resampling methods developed in the last twenty years; (2) to
provide a guide for applied statisticians: practitioners often use (or
misuse) the resampling methods in situations where no theoretical
confirmation has been made; and (3) to stimulate the use of the
jackknife and bootstrap and further devel- opments of the resampling
methods. The theoretical properties of the jackknife and bootstrap
methods are studied in this book in an asymptotic framework. Theorems
are illustrated by examples. Finite sample properties of the jackknife
and bootstrap are mostly investigated by examples and/or empirical
simulation studies. In addition to the theory for the jackknife and
bootstrap methods in problems with independent and identically
distributed (Li.d.) data, we try to cover, as much as we can, the
applications of the jackknife and bootstrap in various complicated
non-Li.d. data problems.
