Jun 27, 2012 · Abstract:The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large ...
Abstract. The bootstrap provides a simple and pow- erful means of assessing the quality of esti- mators. However, in settings involving large datasets ...
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10 References · A scalable bootstrap for massive data · More Efficient Bootstrap Computations · How Many Bootstraps · Richardson Extrapolation and the Bootstrap.
The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets, the computation of ...
Expected number of distinct points in a bootstrap resample is. ∼ 0.632n. Resources required to compute estimate generally scale in number of distinct data ...
Summary. The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large data sets—which are ...
Nov 21, 2011 · The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving very large ...
When a machine learning model is built based on bootstrapped data, the model is trained on the bootstrapped data and then tested on the out of bag (OOB) data.
Feb 14, 2023 · Abstract. The bootstrap is a widely used procedure for statistical inference because of its simplicity and attractive statistical properties ...
Dec 21, 2011 · Abstract:The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large ...
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