This article surveys bootstrap methods for producing good approximate confidence intervals.
The goal is to improve by an order of magnitude upon the accuracy of the standard intervals …

A critical review is given of recent research activity on bootstrap and related procedures.
Theoretical work has shown the bootstrap approach to be a potentially powerful addition to …

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 …

Modern statistical methods use complex, sophisticated models that can lead to intractable
computations. Saddlepoint approximations can be the answer. Written from the user's point …

We study bootstrap confidence intervals for three types of parameters in Cox's proportional
hazards model: the regression parameter, the survival function at fixed time points, and the …

The bootstrap is a versatile inference method that has proven powerful in many statistical
problems. However, when applied to modern large-scale models, it could face substantial …

An iterated bootstrap confidence interval requires an additive correction to be made to the
nominal coverage level of an uncorrected interval. Such correction is usually performed …

In some cases, such as in the estimation of impulse responses, it has been found that for
plausible sample sizes the coverage accuracy of single bootstrap confidence intervals can …

The single bootstrap is implemented by using a saddlepoint approximation to determine
estimates for the survival and hazard functions of first-passage times in complicated semi …

The standard forms of bootstrap iteration are very computationally demanding. As a result,
there have been several attempts to alleviate the computational burden by use of …