moonboot: An R Package Implementing m-out-of-n Bootstrap Methods

TL;DR

The paper introduces moonboot, an R package that implements m-out-of-n bootstrap methods for confidence intervals, addressing issues of inconsistency and parameter estimation through simulation-based evaluation.

Contribution

It provides an R package with functions for computing m-out-of-n bootstrap confidence intervals and estimating key parameters, enhancing bootstrap inference.

Findings

moonboot effectively computes bootstrap confidence intervals.

Simulation results compare different m-out-of-n methods.

The package aids in practical bootstrap applications with weaker assumptions.

Abstract

The m-out-of-n bootstrap is a possible workaround to compute confidence intervals for bootstrap inconsistent estimators, because it works under weaker conditions than the n-out-of-n bootstrap. It has the disadvantage, however, that it requires knowledge of an appropriate scaling factor tau(n) and that the coverage probability for finite n depends on the choice of m. This article presents an R package moonboot which implements the computation of m-out-of-n bootstrap confidence intervals and provides functions for estimating the parameters tau(n) and m. By means of Monte Carlo simulations, we evaluate the different methods and compare them for different estimators