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The Automatic Construction of Bootstrap Confidence Intervals

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Version 3 2021-09-29, 16:19
Version 2 2020-03-12, 12:05
Version 1 2020-01-14, 19:15
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posted on 2021-09-29, 16:19 authored by Bradley Efron, Balasubramanian Narasimhan

The standard intervals, for example, θ̂±1.96σ̂ for nominal 95% two-sided coverage, are familiar and easy to use, but can be of dubious accuracy in regular practice. Bootstrap confidence intervals offer an order of magnitude improvement—from first order to second order accuracy. This article introduces a new set of algorithms that automate the construction of bootstrap intervals, substituting computer power for the need to individually program particular applications. The algorithms are described in terms of the underlying theory that motivates them, along with examples of their application. They are implemented in the R package bcaboot. Supplementary materials for this article are available online.

Funding

Research supported in part by National Science Foundation award DMS 1608182 (Bradley Efron). Research supported in part by the Clinical and Translational Science Award 1UL1 RR025744 for the Stanford Center for Clinical and Translational Education and Research (Spectrum) from the National Center for Research Resources, National Institutes of Health and award LM07033 (Balasubramanian Narasimhan).

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