Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Xie, Min"'
Graph neural network (GNN) models play a pivotal role in numerous tasks involving graph-related data analysis. Despite their efficacy, similar to other deep learning models, GNNs are susceptible to adversarial attacks. Even minor perturbations in gra
Externí odkaz:
http://arxiv.org/abs/2406.03833
We introduce a novel meta-analysis framework to combine dependent tests under a general setting, and utilize it to synthesize various microbiome association tests that are calculated from the same dataset. Our development builds upon the classical me
Externí odkaz:
http://arxiv.org/abs/2404.09353
This paper presents a novel method to make statistical inferences for both the model support and regression coefficients in a high-dimensional logistic regression model. Our method is based on the repro samples framework, in which we conduct statisti
Externí odkaz:
http://arxiv.org/abs/2403.09984
Vibrational resonance (VR) is a nonlinear phenomenon in which the system response to a weak signal can be resonantly enhanced by applying a high-frequency modulation signal with an appropriate amplitude. The majority of VR research has focused on amp
Externí odkaz:
http://arxiv.org/abs/2402.18852
Prognostics and health management (PHM) is essential for industrial operation and maintenance, focusing on predicting, diagnosing, and managing the health status of industrial systems. The emergence of the ChatGPT-Like large-scale language model (LLM
Externí odkaz:
http://arxiv.org/abs/2312.14945
Publikováno v:
Chapter 2 from Gi-Joon Nam and Jason Cong, Modern Circuit Placement Best Practices and Results, 2007, Springer, reproduced with permission of Springer Science+Business Media, LLC
The mixed-size placement benchmarks described in this book chapter directly address several of the shortcomings in previously published suboptimality benchmarks. Two new sets of placement examples are constructed, one targeting the role of nonlocal n
Externí odkaz:
http://arxiv.org/abs/2305.16413
In this paper, we present a new and effective simulation-based approach to conduct both finite- and large-sample inference for high-dimensional linear regression models. This approach is developed under the so-called repro samples framework, in which
Externí odkaz:
http://arxiv.org/abs/2209.09299
A class of nonparametric two-sample tests has been proposed in this article. As a generalization of the original \v{S}id\'aks' test, the proposed test statistic is developed as the sum of the maximal precedence and maximal exceedance statistics. Unli
Externí odkaz:
http://arxiv.org/abs/2208.02521
Autor:
Xie, Min-ge, Wang, Peng
This article presents a novel, general, and effective simulation-inspired approach, called {\it repro samples method}, to conduct statistical inference. The approach studies the performance of artificial samples, referred to as {\it repro samples}, o
Externí odkaz:
http://arxiv.org/abs/2206.06421
Linear transformation model provides a general framework for analyzing censored survival data with covariates. The proportional hazards and proportional odds models are special cases of the linear transformation model. In biomedical studies, covariat
Externí odkaz:
http://arxiv.org/abs/2204.12724