2024

最新文章

• Tail Spectral Density Estimation and Its Uncertainty Quantification: Another Look at Tail Dependent Time Series Analysis AI翻译
  作者：Zhang, Ting; Xu, Beibei
  作者单位：University System of Georgia; University of Georgia
  摘要：We consider the estimation and uncertainty quantification of the tail spectral density, which provide a foundation for tail spectral analysis of tail dependent time series. The tail spectral density has a particular focus on serial dependence in the tail, and can reveal dependence information that is otherwise not discoverable by the traditional spectral analysis. Understanding the convergence rate of tail spectral density estimators and finding rigorous ways to quantify their statistical unce...
• Optimal Linear Discriminant Analysis for High-Dimensional Functional Data AI翻译
  作者：Xue, Kaijie; Yang, Jin; Yao, Fang
  作者单位：Nankai University; National Institutes of Health (NIH) - USA; NIH Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD); Peking University
  摘要：Most of existing methods of functional data classification deal with one or a few processes. In this work we tackle classification of high-dimensional functional data, in which each observation is potentially associated with a large number of functional processes, p, which is comparable to or even much larger than the sample size n. The challenge arises from the complex inter-correlation structures among multiple functional processes, instead of a diagonal correlation for a single process. Sin...
• Bootstrapping Extreme Value Estimators AI翻译
  作者：de Haan, Laurens; Zhou, Chen
  作者单位：Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC; Universidade de Lisboa; Tinbergen Institute
  摘要：This article develops a bootstrap analogue of the well-known asymptotic expansion of the tail quantile process in extreme value theory. One application of this result is to construct confidence intervals for estimators of the extreme value index such as the Probability Weighted Moment (PWM) estimator. For the peaks-over-threshold method, we show the bootstrap consistency of the confidence intervals. By contrast, the asymptotic expansion of the quantile process of the bootstrapped block maxima ...
• Models for Multi-State Survival Data: Rates, Risks, and Pseudo-Values AI翻译
  作者：Prentice, Ross L.
  作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center
• Generalized Bayesian Inference for Discrete Intractable Likelihood AI翻译
  作者：Matsubara, Takuo; Knoblauch, Jeremias; Briol, Francois-Xavier; Oates, Chris. J.
  作者单位：University of Edinburgh; University of London; University College London; Newcastle University - UK; Alan Turing Institute
  摘要：Discrete state spaces represent a major computational challenge to statistical inference, since the computation of normalization constants requires summation over large or possibly infinite sets, which can be impractical. This article addresses this computational challenge through the development of a novel generalized Bayesian inference procedure suitable for discrete intractable likelihood. Inspired by recent methodological advances for continuous data, the main idea is to update beliefs abo...
• Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs AI翻译
  作者：Yang, Liuqing; Zhou, Yongdao; Fu, Haoda; Liu, Min-Qian; Zheng, Wei
  作者单位：Nankai University; Eli Lilly; University of Tennessee System; University of Tennessee Knoxville; University of Tennessee System; University of Tennessee Knoxville
  摘要：Shapley value is originally a concept in econometrics to fairly distribute both gains and costs to players in a coalition game. In the recent decades, its application has been extended to other areas such as marketing, engineering and machine learning. For example, it produces reasonable solutions for problems in sensitivity analysis, local model explanation toward the interpretable machine learning, node importance in social network, attribution models, etc. However, it could be very expensiv...
• Variable Selection for High-Dimensional Nodal Attributes in Social Networks with Degree Heterogeneity AI翻译
  作者：Wang, Jia; Cai, Xizhen; Niu, Xiaoyue; Li, Runze
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Williams College; Williams College
  摘要：We consider a class of network models, in which the connection probability depends on ultrahigh-dimensional nodal covariates (homophily) and node-specific popularity (degree heterogeneity). A Bayesian method is proposed to select nodal features in both dense and sparse networks under a mild assumption on popularity parameters. The proposed approach is implemented via Gibbs sampling. To alleviate the computational burden for large sparse networks, we further develop a working model in which par...
• Modeling and Active Learning for Experiments with Quantitative-Sequence Factors AI翻译
  作者：Xiao, Qian; Wang, Yaping; Mandal, Abhyuday; Deng, Xinwei
  作者单位：University System of Georgia; University of Georgia; East China Normal University; Virginia Polytechnic Institute & State University
  摘要：A new type of experiment that aims to determine the optimal quantities of a sequence of factors is eliciting considerable attention in medical science, bioengineering, and many other disciplines. Such studies require the simultaneous optimization of both quantities and the sequence orders of several components which are called quantitative-sequence (QS) factors. Given the large and semi-discrete solution spaces in such experiments, efficiently identifying optimal or near-optimal solutions by u...
• Randomization-based Joint Central Limit Theorem and Efficient Covariate Adjustment in Randomized Block 2K Factorial Experiments AI翻译
  作者：Liu, Hanzhong; Ren, Jiyang; Yang, Yuehan
  作者单位：Tsinghua University; Central University of Finance & Economics
  摘要：Randomized block factorial experiments are widely used in industrial engineering, clinical trials, and social science. Researchers often use a linear model and analysis of covariance to analyze experimental results; however, limited studies have addressed the validity and robustness of the resulting inferences because assumptions for a linear model might not be justified by randomization in randomized block factorial experiments. In this article, we establish a new finite population joint cent...
• A New and Unified Family of Covariate Adaptive Randomization Procedures and Their Properties AI翻译
  作者：Ma, Wei; Li, Ping; Zhang, Li-Xin; Hu, Feifang
  作者单位：Renmin University of China; Zhejiang University; Zhejiang University; George Washington University
  摘要：In clinical trials and other comparative studies, covariate balance is crucial for credible and efficient assessment of treatment effects. Covariate adaptive randomization (CAR) procedures are extensively used to reduce the likelihood of covariate imbalances occurring. In the literature, most studies have focused on balancing of discrete covariates. Applications of CAR with continuous covariates remain rare, especially when the interest goes beyond balancing only the first moment. In this arti...