A Cheap Bootstrap Method for Fast Inference

Understand

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 computation demand from repeated data resampling and model fitting.
• We present a bootstrap methodology that uses minimal computation, namely with a resample effort as low as one Monte Carlo replication, while maintaining desirable statistical guarantees.
• We present the theory of this method that uses a twisted perspective from the standard bootstrap principle.