552期

最新文章

• Posterior Predictive Design for Phase I Clinical Trials AI翻译
  作者：Fu, Chenqi; Zhou, Shouhao; Lee, J. Jack
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Penn State Health; University of Texas System; UTMD Anderson Cancer Center
  摘要：Interval-based designs represent cutting-edge adaptive methodologies for phase I clinical trials to identify the maximum tolerated dose (MTD). These designs exhibit robust performance comparable to more intricate, model-based designs, and their pretabulated decision rule enables them to be implemented as simply as the conventional algorithm-based designs. In this paper, we introduce the posterior predictive (PoP) design, a novel interval-based design that leverages advanced Bayesian predictive...
• Positive and Unlabeled Data: Model, Estimation, Inference, and Classification AI翻译
  作者：Liu, Siyan; Yeh, Chi-Kuang; Zhang, Xin; Tian, Qinglong; Li, Pengfei
  作者单位：East China Normal University; University of Waterloo
  摘要：This study introduces a new approach to addressing the positive and unlabeled (PU) data through the double exponential tilting model (DETM) under a transfer learning framework. Traditional methods often fall short because they only apply to the common distributions (CD) PU data (also known as the selected completely at random PU data), where the labeled positive and unlabeled positive data are assumed to be from the same distribution. In contrast, our DETM's dual structure effectively accommod...
• Estimating Racial Disparities When Race is Not Observed AI翻译
  作者：McCartan, Cory; Fisher, Robin; Goldin, Jacob; Ho, Daniel E.; Imai, Kosuke
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; United States Department of the Treasury; University of Chicago; National Bureau of Economic Research; Stanford University; Stanford University; Harvard University; Harvard University
  摘要：Estimating racial disparities without access to individual-level racial information is a common challenge in economic and policy settings. We develop a statistical method that relaxes the strong independence assumption of common race imputation approaches like Bayesian-Improved Surname Geocoding (BISG). Our identification assumption is that surname is conditionally independent of the outcome given (unobserved) race, residence location, and other observed characteristics. The proposed approach ...
• 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...
• 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...
• 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...
• Random Effects Model-Based Sufficient Dimension Reduction for Independent Clustered Data AI翻译
  作者：Nghiem, Linh H.; Hui, Francis K. C.
  作者单位：University of Sydney; Australian National University
  摘要：Sufficient dimension reduction (SDR) is a popular class of regression methods which aim to find a small number of linear combinations of covariates that capture all the information of the responses, that is, a central subspace. The majority of current methods for SDR focus on the setting of independent observations, while the few techniques that have been developed for clustered data assume the linear transformation is identical across clusters. In this article, we introduce random effects SDR...
• Dependent Random Partitions by Shrinking Toward an Anchor AI翻译
  作者：Dahl, David B.; Warr, Richard L.; Jensen, Thomas P.
  作者单位：Brigham Young University; Berry Consultants, LLC
  摘要：Although exchangeable processes from Bayesian nonparametrics have been used as a generating mechanism for random partition models, we deviate from this paradigm to explicitly incorporate clustering information in the formulation of our random partition model. Our shrinkage partition distribution takes any partition distribution and shrinks its probability mass toward a specific anchor partition. We show how this provides a framework to model hierarchically-dependent and temporally-dependent ra...