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Ensemble learning is widely used in applications to make predictions in complex decision problems---for example, averaging models fitted to a sequence of samples bootstrapped from the available training data.
Jackknife-after-bootstrap standard errors and influence functions
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Distribution-free predictive inference for regression
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