Journal of the American Statistical Association

最新文章

• Sparse Bayesian Multidimensional Item Response Theory AI翻译
  作者：Li, Jiguang; Gibbons, Robert; Rockova, Veronika
  作者单位：University of Chicago; University of Chicago
  摘要：Multivariate Item Response Theory (MIRT) is sought-after widely by applied researchers looking for interpretable (sparse) explanations underlying response patterns in questionnaire data. There is, however, an unmet demand for such sparsity discovery tools in practice. Our article develops a Bayesian platform for binary and ordinal item MIRT which requires minimal tuning and scales well on large datasets due to its parallelizable features. Bayesian methodology for MIRT models has traditionally ...
• Soccer Analytics: An Introduction Using R AI翻译
  作者：Aue, Alexander
  作者单位：University of California System; University of California Davis
• Channeling Multimodality Through a Unimodalizing Transport: Warp-U Sampler and Stochastic Bridge Sampling Estimator AI翻译
  作者：Ding, Fei; He, Shiyuan; Jones, David E.; Meng, Xiao-Li
  作者单位：Texas A&M University System; Texas A&M University College Station; Beijing Technology & Business University; Harvard University
  摘要：Monte Carlo integration is a powerful tool for scientific and statistical computation, but faces significant challenges when multi-modal distributions are involved, even when the mode locations are known. This work introduces novel Monte Carlo sampling and integration estimation strategies for the multi-modal context by leveraging a generalized version of the stochastic Warp-U transformation (Wang, Jones, and Meng). We propose two flexible classes of Warp-U transformations, one based on a gene...
• Dirichlet Process Mixtures of Block g Priors for Model Selection and Prediction in Linear Models AI翻译
  作者：Porwal, Anupreet; Rodriguez, Abel
  作者单位：Alphabet Inc.; Google Incorporated; University of Washington; University of Washington Seattle
  摘要：This article introduces Dirichlet process mixtures of block g priors for model selection and prediction in linear models. These priors are extensions of traditional mixtures of g priors that allow for differential shrinkage for various (data-selected) blocks of parameters while fully accounting for the predictors' correlation structure, providing a bridge between the literatures on model selection and continuous shrinkage priors. We show that Dirichlet process mixtures of block g priors are co...
• Mixture Modeling for Temporal Point Processes with Memory AI翻译
  作者：Zheng, Xiaotian; Kottas, Athanasios; Sanso, Bruno
  作者单位：University System of Georgia; University of Georgia; University of California System; University of California Santa Cruz
  摘要：We propose a constructive approach to building temporal point processes that incorporate dependence on their history. The dependence is modeled through the conditional density of the duration, that is, the interval between successive event times, using a mixture of first-order conditional densities for each one of a specific number of lagged durations. Such a formulation for the conditional duration density accommodates high-order dynamics, and it thus enables flexible modeling for point proce...
• Robust Microbial Signature Discovery via Post-Selection Inference for Microbiome Compositions AI翻译
  作者：Wang, Weihao; Xu, Xiangnan; Zhao, Hongyu; Wang, Tao
  作者单位：Shanghai Jiao Tong University; University of Sydney; Yale University; Shanghai Jiao Tong University
  摘要：Identifying taxa associated with host phenotypes is crucial for understanding host-microbe interactions and their underlying molecular mechanisms. However, analyzing microbiome data presents unique challenges, as the observed abundances of taxa are high-dimensional, compositional, and subject to both sample-specific and taxon-specific biases. Many existing methods for differential abundance testing struggle to balance false discovery rate control with statistical power. In this article, we pro...
• Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA AI翻译
  作者：Weylandt, Michael; Michailidis, George
  作者单位：City University of New York (CUNY) System; Baruch College (CUNY); University of California System; University of California Los Angeles
  摘要：Network data are commonly collected in a variety of applications, representing either directly measured or statistically inferred connections between subjects or features of interest. In an increasing number of domains, these networks are collected over time, such as repeated interactions between users of a social media platform, or across multiple subjects, such as in multi-subject neuroimaging studies. When analyzing multiple large networks, dimensionality reduction techniques are often used...
• Towards Better Statistical Understanding of Watermarking LLMs AI翻译
  作者：Cai, Zhongze; Liu, Shang; Wang, Hanzhao; Zhong, Huaiyang; Li, Xiaocheng
  作者单位：Imperial College London; University of Sydney; Virginia Polytechnic Institute & State University
  摘要：In this article, we study the problem of watermarking large language models (LLMs). We consider the tradeoff between model distortion and detection ability and formulate it as a constrained optimization problem based on the red-green list watermarking algorithm. We show that the optimal solution to the optimization problem enjoys a nice analytical property which provides a better understanding and inspires the algorithm design for the watermarking process. We develop an online dual gradient as...
• Causality-Oriented Robustness: Exploiting General Noise Interventions AI翻译
  作者：Shen, Xinwei; Buhlmann, Peter; Taeb, Armeen
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Washington; University of Washington Seattle
  摘要：Since distribution shifts are common in real-world applications, there is a pressing need to develop prediction models that are robust against such shifts. Existing frameworks, such as empirical risk minimization or distributionally robust optimization, either lack generalizability for unseen distributions or rely on postulated distance measures. Alternatively, causality offers a data-driven and structural perspective to robust predictions. However, the assumptions necessary for causal inferen...
• Transfer Learning Under Large-Scale Low-Rank Regression Models AI翻译
  作者：Park, Seyoung; Lee, Eun Ryung; Kim, Hyunjin; Zhao, Hongyu
  作者单位：Yonsei University; Yonsei University; Sungkyunkwan University (SKKU); Yale University
  摘要：In high-dimensional multiple response regression problems, the large dimensionality of the coefficient matrix poses a challenge to parameter estimation. To address this challenge, low-rank matrix estimation methods have been developed to facilitate parameter estimation in the high-dimensional regime, where the number of parameters increases with sample size. Despite these methodological advances, accurately predicting multiple responses with limited target data remains a difficult task. To gai...