Journal of the American Statistical Association

最新文章

• Our Mission in Action: Past, Present, and Future AI翻译
  作者：Price, Dionne L.
  作者单位：US Food & Drug Administration (FDA)
  摘要：Driven by our mission, we have advanced science for the public good. Our history is rich in theory, methodology, and applications that are instrumental in solving real-world problems. Our reach is boundless, and our impact is undeniable in diverse areas, including engineering, economics, the environment, genetics, public health, and health policy. As the data landscape continues to evolve, our leadership, expertise and knowledge will be essential to meet the global challenges of the future. We...
• Discussion on Data Fission: Splitting a Single Data Point AI翻译
  作者：Zhao, Zhigen
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
• Representation Retrieval Learning for Heterogeneous Data Integration AI翻译
  作者：Xu, Qi; Qu, Annie
  作者单位：Carnegie Mellon University; University of California System; University of California Santa Barbara
  摘要：In the era of big data, large-scale, multi-source, multi-modality datasets are increasingly ubiquitous, offering unprecedented opportunities for predictive modeling and scientific discovery. However, these datasets often exhibit complex heterogeneity, such as covariates shift, posterior drift, and blockwise missingness, which worsen predictive performance of existing supervised learning algorithms. To address these challenges simultaneously, we propose a novel Representation Retrieval ( R-2 ) ...
• Robust Small Area Estimation: Methods, Theory, Applications, and Open Problems AI翻译
  作者：Bersson, Elizabeth
  作者单位：Massachusetts Institute of Technology (MIT)
• Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data AI翻译
  作者：Gu, Yifan; Yang, Hanfang; Yang, Songshan; Zou, Hui
  作者单位：Renmin University of China; Renmin University of China; Renmin University of China; University of Minnesota System; University of Minnesota Twin Cities
  摘要：In modern data analysis, an improvement in statistical efficiency is expected via effective collaboration among multiple data holders with non-shared data. In this article, we propose a collaborative score-type test (CST) for testing linear hypotheses, which accommodates potentially high-dimensional nuisance parameters and a diverging number of constraints and target parameters. Through a careful decomposition of the Kiefer-Bahadur representation for the traditional score statistic, we identif...
• A Semi-Parametric Model for Assessing the Effect of Temperature on Ice Accumulation Rate From Antarctic Ice Core Data AI翻译
  作者：Srivastava, Radhendushka; Sengupta, Debasis
  作者单位：Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bombay
  摘要：In this article, we present a semiparametric model for describing the effect of temperature on Antarctic ice accumulation on a paleoclimatic time scale. The model is motivated by sharp ups and downs in the rate of ice accumulation apparent from ice core data records, which are synchronous with movements of temperature. We prove consistency of the estimators under reasonable conditions. We conduct extensive simulations to assess the performance of the estimators and their bootstrap based standa...
• Staleness Factors and Volatility Estimation at High Frequencies AI翻译
  作者：Kong, Xinbing; Wu, Bin; Ye, Wuyi
  作者单位：Southeast University - China; Chinese Academy of Sciences; University of Science & Technology of China, CAS
  摘要：In this article, we propose a price staleness factor model that accounts for pervasive market friction across assets and incorporates relevant covariates. Using large-panel high-frequency data, we derive the maximum likelihood estimators of the regression coefficients, the nonstationary factors, and their loading parameters. These estimators recover the time-varying price staleness probabilities. We develop asymptotic theory in which both the dimension d and the sampling frequency n tend to in...
• Spatial Scale-Aware Tail Dependence Modeling for High-Dimensional Spatial Extremes AI翻译
  作者：Shi, Muyang; Zhang, Likun; Risser, Mark D.; Shaby, Benjamin A.
  作者单位：Colorado State University System; Colorado State University Fort Collins; University of Missouri System; University of Missouri Columbia; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory
  摘要：Extreme events over large spatial domains may exhibit highly heterogeneous tail dependence characteristics, yet most existing spatial extremes models yield only one dependence class over the entire spatial domain. To accurately characterize dependence in extreme events, we propose a mixture model that achieves flexible dependence properties and allows high-dimensional inference (similar to 600 spatial locations in our data example) for extremes of spatial processes. We modify the popular rando...
• Optimal Plug-in Gaussian Processes for Modeling Derivatives AI翻译
  作者：Liu, Zejian; Li, Meng
  作者单位：Rice University
  摘要：Derivatives are a key nonparametric functional in wide-ranging applications where the rate of change of an unknown function is of interest. In the Bayesian paradigm, Gaussian processes (GPs) are routinely used as a flexible prior for unknown functions, and are arguably one of the most popular tools in many areas. However, little is known about the optimal modeling strategy and theoretical properties when using GPs for derivatives. In this article, we study a plug-in strategy by differentiating...
• Ball Impurity: Measuring Heterogeneity in General Metric Spaces AI翻译
  作者：Che, Menglu; Li, Ting; Pan, Wenliang; Wang, Xueqin; Zhang, Heping
  作者单位：Yale University; Southern University of Science & Technology; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS
  摘要：Data in various domains, such as neuroimaging and network data analysis, often come in complex forms without possessing a Hilbert structure. The complexity necessitates innovative approaches for effective analysis. We propose a novel measure of heterogeneity, ball impurity, which is designed to work with complex non-Euclidean objects. Our approach extends the notion of impurity to general metric spaces, providing a versatile tool for feature selection and tree models. The ball impurity measure...