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 bias-corrected bootstrap confidence interval (BCBCI) was once the method of choice for
conducting inference on the indirect effect in mediation analysis due to its high power in …

Describing the taste of water is a challenge since drinking water is supposed to have almost
no taste. In this study, different classical sensory methodologies have been applied in order …

The bootstrap is a popular data-driven method to quantify statistical uncertainty, but for
modern high-dimensional problems, it could suffer from huge computational costs due to the …

The parametric and nonparametric approaches to the bootstrap are compared as to their
performance in estimating uncertainties in extreme-value models. Simulation experiments …

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 …

A hands-on guide to using R to carry out key statistical practices in educational and
behavioral sciences research Computing has become an essential part of the day-to-day …

This paper considers the problem of constructing confidence intervals for a single parameter
θ in a multiparameter or nonparametric family. Hybrid resampling methods, which" …

Bootstrap diagnostics are used to assess the reliability of bootstrap calculations and may
suggest useful modified calculations when these are possible. Concern focuses on …