49 citations · 103 across the 19 of their papers we have counts for
8 papers · 1 filter
A Bootstrap Method for Spectral Statistics in High-Dimensional Elliptical Models
Siyao Wang, Miles E. Lopes
Although there is an extensive literature on the eigenvalues of high-dimensional sample covariance matrices, much of it is specialized to independent components (IC) models -- in w…
Improved Rates of Bootstrap Approximation for the Operator Norm: A Coordinate-Free Approach
Miles E. Lopes
Let denote the sample covariance operator of centered i.i.d.~observations in a real separable Hilbert space, and let…
Central Limit Theorem and Bootstrap Approximation in High Dimensions: Near Rates via Implicit Smoothing
Miles E. Lopes
Non-asymptotic bounds for Gaussian and bootstrap approximation have recently attracted significant interest in high-dimensional statistics. This paper studies Berry-Esseen bounds f…
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.…
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…
Bootstrapping Max Statistics in High Dimensions: Near-Parametric Rates Under Weak Variance Decay and Application to Functional and Multinomial Data
Miles E. Lopes, Zhenhua Lin, Hans-Georg Mueller
In recent years, bootstrap methods have drawn attention for their ability to approximate the laws of "max statistics" in high-dimensional problems. A leading example of such a stat…
A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs
Miles E. Lopes
We study the residual bootstrap (RB) method in the context of high-dimensional linear regression. Specifically, we analyze the distributional approximation of linear contrasts $c^{…
A More Powerful Two-Sample Test in High Dimensions using Random Projection
Miles E. Lopes, Laurent J. Jacob, Martin J. Wainwright
We consider the hypothesis testing problem of detecting a shift between the means of two multivariate normal distributions in the high-dimensional setting, allowing for the data di…