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Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression
We investigate popular resampling methods for estimating the uncertainty of statistical models, such as subsampling, bootstrap and the jackknife, and their performance in high-dimensional supervised regression tasks.
Lower Bounds on the Bayesian Risk via Information Measures
We are thus able to provide estimator-independent impossibility results thanks to the Data-Processing Inequalities that divergences satisfy.
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