Zobrazeno 1 - 10
of 721
pro vyhledávání: '"62g35"'
Autor:
Chen, Hantao, Wang, Cheng
This paper is concerned with Spearman's correlation matrices under large dimensional regime, in which the data dimension diverges to infinity proportionally with the sample size. We establish the central limit theorem for the linear spectral statisti
Externí odkaz:
http://arxiv.org/abs/2411.15861
Recent work has used optimal transport ideas to generalize the notion of (center-outward) quantiles to dimension $d\geq 2$. We study the robustness properties of these transport-based quantiles by deriving their breakdown point, roughly, the smallest
Externí odkaz:
http://arxiv.org/abs/2410.16554
Conditional quantile treatment effect (CQTE) can provide insight into the effect of a treatment beyond the conditional average treatment effect (CATE). This ability to provide information over multiple quantiles of the response makes CQTE especially
Externí odkaz:
http://arxiv.org/abs/2410.12454
Autor:
Minsker, Stanislav, Shen, Yinan
Is there a natural way to order data in dimension greater than one? The approach based on the notion of data depth, often associated with the name of John Tukey, is among the most popular. Tukey's depth has found applications in robust statistics, gr
Externí odkaz:
http://arxiv.org/abs/2410.00219
This paper analyzes the statistical performance of a robust spectral clustering method for latent structure recovery in noisy data matrices. We consider eigenvector-based clustering applied to a matrix of nonparametric rank statistics that is derived
Externí odkaz:
http://arxiv.org/abs/2408.10136
Autor:
Lee, Jongmin, Jung, Sungkyu
This article introduces Huber means on Riemannian manifolds, providing a robust alternative to the Frechet mean by integrating elements of both square and absolute loss functions. The Huber means are designed to be highly resistant to outliers while
Externí odkaz:
http://arxiv.org/abs/2407.15764
Autor:
Morris, Rachel, Murray, Ryan
In recent years there has been significant interest in the effect of different types of adversarial perturbations in data classification problems. Many of these models incorporate the adversarial power, which is an important parameter with an associa
Externí odkaz:
http://arxiv.org/abs/2406.14682
In the mean-median-mode triad of univariate centrality measures, the mode has been overlooked for estimating the center of symmetry in continuous and unimodal settings. This paper expands on the connection between kernel mode estimators and M-estimat
Externí odkaz:
http://arxiv.org/abs/2406.08241
Given the vast number of classifiers that have been (and continue to be) proposed, reliable methods for comparing them are becoming increasingly important. The desire for reliability is broken down into three main aspects: (1) Comparisons should allo
Externí odkaz:
http://arxiv.org/abs/2406.03924
In this article, we propose L-estimators of the unknown parameters in the instrumental variables regression in the presence of censored data under endogeneity. We allow the random errors involved in the model to be dependent. The proposed estimation
Externí odkaz:
http://arxiv.org/abs/2405.19145