Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Lopes, Miles E."'
Autor:
Liu, Mingshuo, Lopes, Miles E.
Although much progress has been made in the theory and application of bootstrap approximations for max statistics in high dimensions, the literature has largely been restricted to cases involving light-tailed data. To address this issue, we propose a
Externí odkaz:
http://arxiv.org/abs/2409.16683
Autor:
Wang, Siyao, Lopes, Miles E.
Due to the broad applications of elliptical models, there is a long line of research on goodness-of-fit tests for empirically validating them. However, the existing literature on this topic is generally confined to low-dimensional settings, and to th
Externí odkaz:
http://arxiv.org/abs/2408.05514
Autor:
Murray, Riley, Demmel, James, Mahoney, Michael W., Erichson, N. Benjamin, Melnichenko, Maksim, Malik, Osman Asif, Grigori, Laura, Luszczek, Piotr, Dereziński, Michał, Lopes, Miles E., Liang, Tianyu, Luo, Hengrui, Dongarra, Jack
Randomized numerical linear algebra - RandNLA, for short - concerns the use of randomization as a resource to develop improved algorithms for large-scale linear algebra computations. The origins of contemporary RandNLA lay in theoretical computer sci
Externí odkaz:
http://arxiv.org/abs/2302.11474
Random Fourier Features (RFF) is among the most popular and broadly applicable approaches for scaling up kernel methods. In essence, RFF allows the user to avoid costly computations on a large kernel matrix via a fast randomized approximation. Howeve
Externí odkaz:
http://arxiv.org/abs/2302.11174
Autor:
Wang, Siyao, Lopes, Miles E.
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 which observations are represented as linear transformations of random
Externí odkaz:
http://arxiv.org/abs/2209.03556
Autor:
Lopes, Miles E.
Let $\hat\Sigma=\frac{1}{n}\sum_{i=1}^n X_i\otimes X_i$ denote the sample covariance operator of centered i.i.d.~observations $X_1,\dots,X_n$ in a real separable Hilbert space, and let $\Sigma=\mathbb{E}(X_1\otimes X_1)$. The focus of this paper is t
Externí odkaz:
http://arxiv.org/abs/2208.03050
Autor:
Buluc, Aydin, Kolda, Tamara G., Wild, Stefan M., Anitescu, Mihai, DeGennaro, Anthony, Jakeman, John, Kamath, Chandrika, Kannan, Ramakrishnan, Lopes, Miles E., Martinsson, Per-Gunnar, Myers, Kary, Nelson, Jelani, Restrepo, Juan M., Seshadhri, C., Vrabie, Draguna, Wohlberg, Brendt, Wright, Stephen J., Yang, Chao, Zwart, Peter
Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion
Externí odkaz:
http://arxiv.org/abs/2104.11079
Autor:
Yao, Junwen, Lopes, Miles E.
In the context of principal components analysis (PCA), the bootstrap is commonly applied to solve a variety of inference problems, such as constructing confidence intervals for the eigenvalues of the population covariance matrix $\Sigma$. However, wh
Externí odkaz:
http://arxiv.org/abs/2104.07328
Autor:
Lopes, Miles E.
Non-asymptotic bounds for Gaussian and bootstrap approximation have recently attracted significant interest in high-dimensional statistics. This paper studies Berry-Esseen bounds for such approximations with respect to the multivariate Kolmogorov dis
Externí odkaz:
http://arxiv.org/abs/2009.06004
We propose a new approach to the problem of high-dimensional multivariate ANOVA via bootstrapping max statistics that involve the differences of sample mean vectors. The proposed method proceeds via the construction of simultaneous confidence regions
Externí odkaz:
http://arxiv.org/abs/2007.01058