A scalable bootstrap for massive data
@article{Kleiner2011ASB, title={A scalable bootstrap for massive data}, author={Ariel Kleiner and Ameet S. Talwalkar and Purnamrita Sarkar and Michael I. Jordan}, journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, year={2011}, volume={76} }
The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large data sets—which are increasingly prevalent—the calculation of bootstrap‐based quantities can be prohibitively demanding computationally. Although variants such as subsampling and the m out of n bootstrap can be used in principle to reduce the cost of bootstrap computations, these methods are generally not robust to specification of tuning parameters (such as the number…
321 Citations
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