2024

最新文章

• Extended fiducial inference: toward an automated process of statistical inference AI翻译
  作者：Liang, Faming; Kim, Sehwan; Sun, Yan
  作者单位：Purdue University System; Purdue University; Harvard University; Harvard Medical School; University of Pennsylvania
  摘要：While fiducial inference was widely considered a big blunder by R.A. Fisher, the goal he initially set-'inferring the uncertainty of model parameters on the basis of observations'-has been continually pursued by many statisticians. To this end, we develop a new statistical inference method called extended Fiducial inference (EFI). The new method achieves the goal of fiducial inference by leveraging advanced statistical computing techniques while remaining scalable for big data. Extended Fiduci...
• Stefano Rizzelli's contribution to the Discussion of 'Safe testing' by Grünwald, de Heide, and Koolen AI翻译
  作者：Rizzelli, Stefano
  作者单位：University of Padua
• Jorge Mateu's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Mateu, Jorge
  作者单位：Universitat Jaume I
• Estimating means of bounded random variables by betting AI翻译
  作者：Neu, Gergely
  作者单位：Pompeu Fabra University
  摘要：We derive confidence intervals (CIs) and confidence sequences (CSs) for the classical problem of estimating a bounded mean. Our approach generalizes and improves on the celebrated Chernoff method, yielding the best closed-form empirical-Bernstein CSs and CIs (converging exactly to the oracle Bernstein width) as well as non-closed-form betting CSs and CIs. Our method combines new composite nonnegative (super)martingales with Ville's maximal inequality, with strong connections to testing by bett...
• Isadora Antoniano Villalobos's contribution to the Discussion of 'Martingale Posterior Distributions' by Fong, Holmes and Walker AI翻译
  作者：Antoniano-Villalobos, Isadora
  作者单位：Universita Ca Foscari Venezia
• David Huk, Lorenzo Pacchiardi, Ritabrata Dutta and Mark Steel's contribution to the Discussion of 'Martingale posterior distributions' by Fong, Holmes and Walker AI翻译
  作者：Huk, David; Pacchiardi, Lorenzo; Dutta, Ritabrata; Steel, Mark
  作者单位：University of Warwick; University of Oxford
• Controlling the false discovery rate in transformational sparsity: Split Knockoffs AI翻译
  作者：Cao, Yang; Sun, Xinwei; Yao, Yuan
  作者单位：Hong Kong University of Science & Technology; Fudan University
  摘要：Controlling the False Discovery Rate (FDR) in a variable selection procedure is critical for reproducible discoveries, and it has been extensively studied in sparse linear models. However, it remains largely open in scenarios where the sparsity constraint is not directly imposed on the parameters but on a linear transformation of the parameters to be estimated. Examples of such scenarios include total variations, wavelet transforms, fused LASSO, and trend filtering. In this paper, we propose a...
• Stratification pattern enumerator and its applications AI翻译
  作者：Tian, Ye; Xu, Hongquan
  作者单位：Beijing University of Posts & Telecommunications; University of California System; University of California Los Angeles
  摘要：Space-filling designs are widely used in computer experiments. A minimum aberration-type space-filling criterion was recently proposed to rank and assess a family of space-filling designs including orthogonal array-based Latin hypercubes and strong orthogonal arrays. However, it is difficult to apply the criterion in practice because it requires intensive computation for determining the space-filling pattern, which measures the stratification properties of designs on various subregions. In thi...
• Core shrinkage covariance estimation for matrix-variate data AI翻译
  作者：Hoff, Peter; McCormack, Andrew; Zhang, Anru R.
  作者单位：Duke University; Duke University
  摘要：A separable covariance model can describe the among-row and among-column correlations of a random matrix and permits likelihood-based inference with a very small sample size. However, if the assumption of separability is not met, data analysis with a separable model may misrepresent important dependence patterns in the data. As a compromise between separable and unstructured covariance estimation, we decompose a covariance matrix into a separable component and a complementary 'core' covariance...