Abstract
The bootstrap is used to obtain statistical inference (confidence intervals, hypothesis tests) in a wide variety of settings (Efron and Tibshirani 1993; Davison and Hinkley 1997). Bootstrap-based confidence intervals have been shown in some settings to have higher-order accuracy compared to Wald-style intervals based on the normal approximation (Hall 1988, 1992; DiCiccio and Romano 1988). For this reason it has been widely adopted as a method for generating inference in a range of contexts, not all of which have theoretical support. One setting in which it fails to work in the manner it is typically applied is in the framework of targeted learning. We describe the reasons for this failure in detail and present a solution in the form of a targeted bootstrap, designed to be consistent for the first two moments of the sampling distribution.
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Coyle, J., van der Laan, M.J. (2018). Targeted Bootstrap. In: Targeted Learning in Data Science. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-65304-4_28
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DOI: https://doi.org/10.1007/978-3-319-65304-4_28
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