Journal of the Royal Statistical Society: Series B

最新文章

• Generalized point process additive models AI翻译
  作者：Lee, Kuang-Yao; Sun, Jiehuan; Li, Bing; Li, Lexin
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of California System; University of California Berkeley
  摘要：In this article, we propose a generalized point process additive model with a scalar response and high-dimensional point process predictors. Our proposal is built upon four key components: a realization of a point process as a random counting measure, a generalized point process regression framework, a new kernel function for random measure through kernel embedding, and a suite of low-dimensional structures including the additive model, reduced basis representation, and sparsity. We develop an...
• Combining evidence across filtrations† AI翻译
  作者：Choe, Yo Joong; Ramdas, Aaditya
  作者单位：INSEAD Business School; Carnegie Mellon University
  摘要：In sequential anytime-valid inference, any admissible procedure must be based on e-processes: generalizations of test martingales that quantify the accumulated evidence against a composite null hypothesis at any stopping time. This paper proposes a method for combining e-processes constructed in different filtrations but for the same null. Although e-processes in the same filtration can be combined effortlessly (by averaging), e-processes in different filtrations cannot because their validity ...
• Spectral change point estimation for high-dimensional time series by sparse tensor decomposition AI翻译
  作者：Zhang, Xinyu; Chan, Kung-Sik
  作者单位：East China Normal University; East China Normal University; University of Iowa
  摘要：Multivariate time series may be subject to partial structural changes over certain frequency band, for instance, in neuroscience. We study the change point detection problem with high-dimensional time series, within the framework of frequency domain. The overarching goal is to locate all change points and delineate which series are activated by the change, over which frequencies. In practice, the number of activated series per change and frequency could span from a few to full participation. W...
• Orthogonalized moment aberration for mixed-level multi-stratum factorial designs with partially-relaxed orthogonal block structures AI翻译
  作者：Chang, Ming-Chung
  作者单位：Academia Sinica - Taiwan
  摘要：Multi-stratum factorial designs, such as block designs and row-column designs, are widely used for screening treatment factors in experiments involving complex structures of experimental units due to multiple sources of error. In this study, we propose a unified model-free approach, termed orthogonalized moment aberration, to compare the similarities between level combinations of treatment factors assigned to heterogeneous experimental units. The proposed approach, which uses kernel functions ...
• Optimized annealed sequential Monte Carlo samplers AI翻译
  作者：Syed, Saifuddin; Bouchard-Cote, Alexandre; Chern, Kevin; Doucet, Arnaud
  作者单位：University of British Columbia; University of Oxford
  摘要：Annealed sequential Monte Carlo (ASMC) samplers are special cases of SMC samplers where the sequence of distributions can be embedded in a smooth path of distributions. Using this underlying path and a performance model based on the variance of the normalizing constant estimator, we systematically study dense-schedule limits. From our theory emerges a notion of global barrier, capturing the inherent complexity of normalizing constant approximation under our performance model. We then turn the ...
• Statistical exploration of the Manifold Hypothesis AI翻译
  作者：He, Yinqiu
  作者单位：University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
  摘要：The manifold hypothesis is a widely accepted tenet of machine learning which asserts that nominally high-dimensional data are in fact concentrated near a low-dimensional manifold, embedded in high-dimensional space. This phenomenon is observed empirically in many real-world situations, has led to development of a wide range of statistical methods in the last few decades, and has been suggested as a key factor in the success of modern AI technologies. We show that rich and sometimes intricate m...
• Regularized halfspace depth for functional data AI翻译
  作者：Yeon, Hyemin; Dai, Xiongtao; Lopez-Pintado, Sara
  作者单位：University System of Ohio; Kent State University; Kent State University Kent; Kent State University Salem; University of California System; University of California Berkeley; Northeastern University
  摘要：Data depth is a powerful tool originally proposed to rank multivariate data from centre outward. In this context, one of the most archetypical depth notions is Tukey's halfspace depth. In the last few decades, notions of depth have also been proposed for functional data. However, a naive extension of Tukey's depth cannot handle functional data because of its degeneracy. Here, we propose a new halfspace depth for functional data, which avoids degeneracy by regularization. The halfspace projecti...
• Selecting informative conformal prediction sets with false coverage rate control AI翻译
  作者：Gazin, Ulysse; Heller, Ruth; Marandon, Ariane; Roquain, Etienne
  作者单位：Universite Paris Cite; Sorbonne Universite; Universite Paris Cite; Centre National de la Recherche Scientifique (CNRS); Tel Aviv University; Alan Turing Institute; Sorbonne Universite; Universite Paris Cite; Centre National de la Recherche Scientifique (CNRS)
  摘要：In supervised learning, including regression and classification, conformal methods provide prediction sets for the outcome/label with finite sample coverage for any machine learning predictor. We consider here the case where such prediction sets come after a selection process. The selection process requires that the selected prediction sets be 'informative' in a well-defined sense. We consider both the classification and regression settings where the analyst may consider as informative only th...
• Augmentation invariant manifold learning AI翻译
  作者：Wang, Shulei
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Data augmentation is a widely used technique and an essential ingredient in the recent advance in self-supervised representation learning. By preserving the similarity between augmented data, the resulting data representation can improve various downstream analyses and achieve state-of-the-art performance in many applications. Despite the empirical effectiveness, most existing methods lack theoretical understanding under a general nonlinear setting. To fill this gap, we develop a statistical f...
• Jiangfeng Wang, Rong Jiang, Keming Yu's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Wang, Jiangfeng; Yu, Keming; Jiang, Rong
  作者单位：Zhejiang Gongshang University; Brunel University; Shanghai University of International Business & Economics
  摘要：We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights-weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients th...