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Title: A cheap bootstrap method for fast inference Authors:  Henry Lam - Columbia University (United States) [presenting]
Abstract: A bootstrap methodology is presented that uses minimal computation in terms of resampling effort, namely as low as one Monte Carlo replication, while maintaining desirable statistical guarantees. We present the theory of this method that uses a simple twist from the standard bootstrap principle. We illustrate how this methodology can be used for fast inference across different estimation problems, and its relevance and generalizations, especially to large-scale statistical problems and computational simulation.