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20162020
most citedA Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs

2 citations · 3 across the 2 of their papers we have counts for

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Showing 2019Show all

3 papers · 1 filter

math.ST2019

Bootstrapping the Operator Norm in High Dimensions: Error Estimation for Covariance Matrices and Sketching

Miles E. Lopes, N. Benjamin Erichson, Michael W. Mahoney

Although the operator (spectral) norm is one of the most widely used metrics for covariance estimation, comparatively little is known about the fluctuations of error in this norm.…

stat.ML2019

Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting

Miles E. Lopes, Suofei Wu, Thomas C. M. Lee

When randomized ensemble methods such as bagging and random forests are implemented, a basic question arises: Is the ensemble large enough? In particular, the practitioner desires…

math.ST2019★ 1 cited

Estimating the Algorithmic Variance of Randomized Ensembles via the Bootstrap

Miles E. Lopes

Although the methods of bagging and random forests are some of the most widely used prediction methods, relatively little is known about their algorithmic convergence. In particula…