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Central Limit Theorem and Bootstrap Approximation in High Dimensions with Near 1/√(n) Rates
Non-asymptotic bounds for Gaussian and bootstrap approximation have rece...
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High-dimensional MANOVA via Bootstrapping and its Application to Functional and Sparse Count Data
We propose a new approach to the problem of high-dimensional multivariat...
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Error Estimation for Sketched SVD via the Bootstrap
In order to compute fast approximations to the singular value decomposit...
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Bootstrapping the Operator Norm in High Dimensions: Error Estimation for Covariance Matrices and Sketching
Although the operator (spectral) norm is one of the most widely used met...
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Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting
When randomized ensemble methods such as bagging and random forests are ...
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Estimating the Algorithmic Variance of Randomized Ensembles via the Bootstrap
Although the methods of bagging and random forests are some of the most ...
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On the Maximum of Dependent Gaussian Random Variables: A Sharp Bound for the Lower Tail
Although there is an extensive literature on the maxima of Gaussian proc...
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Bootstrapping Max Statistics in High Dimensions: Near-Parametric Rates Under Weak Variance Decay and Application to Functional Data Analysis
In recent years, bootstrap methods have drawn attention for their abilit...
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Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
Over the course of the past decade, a variety of randomized algorithms h...
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A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication
In recent years, randomized methods for numerical linear algebra have re...
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A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs
We study the residual bootstrap (RB) method in the context of high-dimen...
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A Sharp Bound on the Computation-Accuracy Tradeoff for Majority Voting Ensembles
When random forests are used for binary classification, an ensemble of t...
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Estimating Unknown Sparsity in Compressed Sensing
In the theory of compressed sensing (CS), the sparsity ||x||_0 of the un...
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A More Powerful Two-Sample Test in High Dimensions using Random Projection
We consider the hypothesis testing problem of detecting a shift between ...
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