549期

最新文章

• Comment on Data Fission: Splitting a Single Data Point AI翻译
  作者：Waggoner, Philip
  作者单位：Columbia University
• Comments on Data Fission: Splitting a Single Data Point by James Leiner, Boyan Duan, Larry Wasserman, and Aaditya Ramdas AI翻译
  作者：Wang, Lijun; Zhao, Hongyu
  作者单位：Yale University
• Operationalizing Legislative Bodies: A Methodological and Empirical Perspective with a Bayesian Approach AI翻译
  作者：Luque, Carolina; Sosa, Juan
  作者单位：Universidad EAN; Universidad Nacional de Colombia
  摘要：This article extensively reviews applications, extensions, and models derived from the Bayesian ideal point estimator. We focus our attention on studies conducted in the United States as well as Latin America. First, we provide a detailed description of the Bayesian ideal point estimator. Next, we propose a new taxonomy to synthesize and frame technical developments and applications associated with the estimator in the context of the United States Congress and Latin American governing bodies. ...
• A Space Astrophysics Perspective on Poisson-FOCuS AI翻译
  作者：Graziani, Carlo
  作者单位：United States Department of Energy (DOE); Argonne National Laboratory
• Randomness of Shapes and Statistical Inference on Shapes via the Smooth Euler Characteristic Transform AI翻译
  作者：Meng, Kun; Wang, Jinyu; Crawford, Lorin; Eloyan, Ani
  作者单位：Brown University; Brown University; Brown University; Microsoft
  摘要：In this article, we establish the mathematical foundations for modeling the randomness of shapes and conducting statistical inference on shapes using the smooth Euler characteristic transform. Based on these foundations, we propose two Chi-squared statistic-based algorithms for testing hypotheses on random shapes. Simulation studies are presented to validate our mathematical derivations and to compare our algorithms with state-of-the-art methods to demonstrate the utility of our proposed frame...
• Comment on Data Fission: Splitting a Single Data Point by James Leiner, Boyan Duan, Larry Wasserman and Aaditya Ramdas AI翻译
  作者：Joseph, V. Roshan
  作者单位：University System of Georgia; Georgia Institute of Technology
• Data Fission: Splitting a Single Data Point AI翻译
  作者：Leiner, James; Duan, Boyan; Wasserman, Larry; Ramdas, Aaditya
  作者单位：Carnegie Mellon University; Alphabet Inc.; Google Incorporated
  摘要：Suppose we observe a random vector X from some distribution in a known family with unknown parameters. We ask the following question: when is it possible to split X into two pieces f(X) and g(X) such that neither part is sufficient to reconstruct X by itself, but both together can recover X fully, and their joint distribution is tractable? One common solution to this problem when multiple samples of X are observed is data splitting, but Rasines and Young offers an alternative approach that use...
• Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interference AI翻译
  作者：Lee, Chanhwa; Zeng, Donglin; Hudgens, Michael G.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of Michigan System; University of Michigan
  摘要：Interference occurs when a unit's treatment (or exposure) affects another unit's outcome. In some settings, units may be grouped into clusters such that it is reasonable to assume that interference, if present, only occurs between individuals in the same cluster, that is, there is clustered interference. Various causal estimands have been proposed to quantify treatment effects under clustered interference from observational data, but these estimands either entail treatment policies lacking rea...
• Modeling and Learning on High-Dimensional Matrix-Variate Sequences AI翻译
  作者：Zhang, Xu; Liu, Catherine C.; Guo, Jianhua; Yuen, K. C.; Welsh, A. H.
  作者单位：South China Normal University; Hong Kong Polytechnic University; Beijing Technology & Business University; University of Hong Kong; Australian National University
  摘要：We propose a new matrix factor model, named RaDFaM, which is strictly derived from the general rank decomposition and assumes a high-dimensional vector factor model structure for each basis vector. RaDFaM contributes a novel class of low-rank latent structures that trade off between signal intensity and dimension reduction from a tensor subspace perspective. Based on the intrinsic separable covariance structure of RaDFaM, for a collection of matrix-valued observations, we derive a new class of...
• Sobolev Calibration of Imperfect Computer Models AI翻译
  作者：Zhang, Qingwen; Wang, Wenjia
  作者单位：Hong Kong University of Science & Technology; Hong Kong University of Science & Technology (Guangzhou); Hong Kong University of Science & Technology; Hong Kong University of Science & Technology
  摘要：Calibration refers to the statistical estimation of unknown model parameters in computer experiments, such that computer experiments can match underlying physical systems. This work develops a new calibration method for imperfect computer models, Sobolev calibration, which can rule out calibration parameters that generate overfitting calibrated functions. We prove that the Sobolev calibration enjoys desired theoretical properties including fast convergence rate, asymptotic normality and semipa...