2025

最新文章

• Bayesian Inference on Brain-Computer Interfaces via GLASS AI翻译
  作者：Zhao, Bangyao; Huggins, Jane E.; Kang, Jian
  作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers. The fundamental statistical problem in P300 BCIs lies in classifying target and non-target stimuli based on electroencephalogram (EEG) signals. However, the low signal-to-noise ratio (SNR) and complex spatial/temporal correlations of EEG signals present challenges in modeling and computation, especially for individuals with severe physical disabilities-BCI's primary use...
• GeoWarp: Warped Spatial Processes for Inferring Subsea Sediment Properties AI翻译
  作者：Bertolacci, Michael; Zammit-Mangion, Andrew; Giraldo, Juan Valderrama; O'Neill, Michael; Bransby, Fraser; Watson, Phil
  作者单位：University of Western Australia; University of Wollongong; University of Western Australia
  摘要：For offshore structures like wind turbines, subsea infrastructure, pipelines, and cables, it is crucial to quantify the properties of the seabed sediments at a proposed site. However, data collection offshore is costly, so analysis of the seabed sediments must be made from measurements that are spatially sparse. Adding to this challenge, the structure of the seabed sediments exhibits both nonstationarity and anisotropy. To address these issues, we propose GeoWarp, a hierarchical spatial statis...
• Network Goodness-of-Fit for the Block-Model Family AI翻译
  作者：Jin, Jiashun; Ke, Zheng Tracy; Tang, Jiajun; Wang, Jingming
  作者单位：Carnegie Mellon University; Harvard University; University of Virginia
  摘要：The block-model family has four popular network models (SBM, DCBM, MMSBM, and DCMM). A fundamental problem is, how well each of these models fits with real networks. We propose GoF-MSCORE as a new Goodness-of-Fit (GoF) metric for DCMM (the broadest one among the four), with two main ideas. The first is to use cycle count statistics as a general recipe for GoF. The second is a novel network fitting scheme. GoF-MSCORE is a flexible GoF approach, and we further extend it to SBM, DCBM, and MMSBM. ...
• Who Are We Missing?: A Principled Approach to Characterizing the Underrepresented Population AI翻译
  作者：Parikh, Harsh; Ross, Rachael K.; Stuart, Elizabeth; Rudolph, Kara E.
  作者单位：Johns Hopkins University; Columbia University
  摘要：Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect heterogeneity and underrepresentation. Our article addresses the critical issue of identifying and characterizing underrepresented subgroups in RCTs, proposing a novel framework for refining target populations to improve generalizability. We introduce an optimization-based approach, Rashomon Set of Optimal Trees (ROOT), ...
• Communication-Efficient Distributed Sparse Learning with Oracle Property and Geometric Convergence AI翻译
  作者：Liu, Weidong; Mao, Xiaojun; Tu, Jiyuan
  作者单位：Shanghai Jiao Tong University; Shanghai Jiao Tong University; Shanghai University of Finance & Economics
  摘要：This article introduces two highly efficient distributed non-convex sparse learning algorithms. Our approach accommodates non-convexity in both the loss function and penalty, acknowledging the potential non-uniqueness of local minimizers due to the inherent non-convexity. The development of an algorithm that ensures convergence to a locally minimal solution with desired statistical properties becomes imperative in this context. To overcome this challenge, we propose a strategy involving the re...
• Robust Bayesian Modeling of Counts with Zero Inflation and Outliers: Theoretical Robustness and Efficient Computation AI翻译
  作者：Hamura, Yasuyuki; Irie, Kaoru; Sugasawa, Shonosuke
  作者单位：Kyoto University; University of Tokyo; Keio University
  摘要：Count data with zero inflation and large outliers are ubiquitous in many scientific applications. However, posterior analysis under a standard statistical model, such as Poisson or negative binomial distribution, is sensitive to such contamination. This study introduces a novel framework for Bayesian modeling of counts that is robust to both zero inflation and large outliers. In doing so, we introduce rescaled beta distribution and adopt it to absorb undesirable effects from zero and outlying ...
• Communication-Efficient Distributed Estimation and Inference for Cox's Model AI翻译
  作者：Bayle, Pierre; Fan, Jianqing; Lou, Zhipeng
  作者单位：Princeton University; University of California System; University of California San Diego
  摘要：Motivated by multi-center biomedical studies that cannot share individual data due to privacy and ownership concerns, we develop communication-efficient iterative distributed algorithms for estimation and inference in the high-dimensional sparse Cox proportional hazards model. We demonstrate that our estimator, even with a relatively small number of iterations, achieves the same convergence rate as the ideal full-sample estimator under very mild conditions. To construct confidence intervals fo...
• Adjacency Matrix Decomposition Clustering for Human Activity Data AI翻译
  作者：Barnard, Martha; Fan, Yingling; Wolfson, Julian
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Mobile apps and wearable devices accurately and continuously measure human activity; patterns within this data can provide a wealth of information applicable to fields such as transportation and health. Despite the potential utility of this data, there has been limited development of analysis methods for sequences of daily activities. In this article, we propose a novel clustering method and cluster evaluation metric for human activity data that leverages an adjacency matrix representation to ...
• Inference in Generalized Linear Models with Robustness to Misspecified Variances AI翻译
  作者：De Santis, Riccardo; Goeman, Jelle J.; Hemerik, Jesse; Davenport, Samuel; Finos, Livio
  作者单位：University of Padua; Leiden University; Leiden University - Excl LUMC; Leiden University Medical Center (LUMC); Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC; University of California System; University of California San Diego
  摘要：Generalized linear models usually assume a common dispersion parameter, an assumption that is seldom true in practice. Consequently, standard parametric methods may suffer appreciable loss of Type I error control. As an alternative, we present a semi-parametric group-invariance method based on sign flipping of score contributions. Our method requires only the correct specification of the mean model, but is robust against any misspecification of the variance. We present tests for single as well...
• Hypothesis Testing for a Functional Parameter via Self-Normalization AI翻译
  作者：Zhang, Yi; Shao, Xiaofeng
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Washington University (WUSTL); Washington University (WUSTL)
  摘要：Testing simple or composite hypothesis on a functional parameter has attracted considerable attention in time series analysis. To accommodate for the unknown temporal dependence, classical nonparametric approaches such as block bootstrapping and subsampling all involve a bandwidth parameter, the choice of which can substantially affect the finite sample performance. The self normalization (SN) method is tuning parameter free when applied to the inference of a finite-dimensional parameter but i...