Journal of the Royal Statistical Society: Series B

最新文章

• Authors' reply to discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Whiteley, Nick; Gray, Annie; Rubin-Delanchy, Patrick
  作者单位：University of Bristol; Alan Turing Institute; University of Edinburgh; Heriot Watt University; University of Edinburgh
• Proximal causal inference for conditional separable effects AI翻译
  作者：Park, Chan; Stensrud, Mats J.; Tchetgen Tchetgen, Eric J.
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Pennsylvania
  摘要：Scientists regularly pose questions about treatment effects on outcomes conditional on a posttreatment event. However, causal inference in such settings requires care, even in perfectly executed randomized experiments. Recently, the conditional separable effect (CSE) was proposed as an interventionist estimand that corresponds to scientifically meaningful questions in these settings. However, existing results for the CSE require no unmeasured confounding between the outcome and posttreatment e...
• A stratified L2-discrepancy with application to space-filling designs 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. We propose a stratified L2-discrepancy to evaluate the uniformity of a design when the design domain is stratified into various subregions. Weights are used to adjust preferences for the uniformity over subregions in each stratification. The stratified L2-discrepancy is easy to compute, satisfies a Koksma-Hlawka type inequality, and overcomes the curse of dimensionality that exists for other discrepancies. It is applicable to a bro...
• Optimal clustering by Lloyd's algorithm for low-rank mixture model AI翻译
  作者：Lyu, Zhongyuan; Xia, Dong
  作者单位：Hong Kong University of Science & Technology
  摘要：This article investigates the computational and statistical limits in clustering matrix-valued observations. We propose a low-rank mixture model (LrMM), adapted from the classical Gaussian mixture model (GMM), to handle matrix-valued observations, assuming low-rankness for population centre matrices. A computationally efficient clustering method is designed by integrating Lloyd's algorithm and low-rank approximation. Once well-initialized, the algorithm converges fast and achieves an exponenti...
• Multi-resolution subsampling for linear classification with massive data AI翻译
  作者：Chen, Haolin; Dette, Holger; Yu, Jun
  作者单位：Beijing Institute of Technology; Ruhr University Bochum
  摘要：Subsampling is one of the popular methods to balance statistical efficiency and computational efficiency in the big data era. Most approaches aim to select informative or representative sample points to achieve good overall information of the full data. The present work takes the view that sampling techniques are recommended for the region we focus on and summary measures are enough to collect the information for the rest according to a well-designed data partitioning. We propose a subsampling...
• Strong oracle guarantees for partial penalized tests of high-dimensional generalized linear models AI翻译
  作者：Jacobson, Tate
  作者单位：Oregon State University
  摘要：Partial penalized tests provide flexible approaches to testing linear hypotheses in high-dimensional generalized linear models. However, because the estimators used in these tests are local minimizers of potentially nonconvex folded-concave penalized objectives, the solutions one computes in practice may not coincide with the unknown local minima for which we have nice theoretical guarantees. To close this gap between theory and computation, we introduce local linear approximation (LLA) algori...
• Selective randomization inference for adaptive experiments AI翻译
  作者：Freidling, Tobias; Zhao, Qingyuan; Gao, Zijun
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Cambridge; University of Southern California
  摘要：Adaptive experiments use preliminary analyses of the data to inform further course of action and are commonly used in many disciplines including medical and social sciences. Because the null hypothesis and experimental design are data-dependent, it has long been recognized that statistical inference for adaptive experiments is not straightforward. Most existing methods only apply to specific adaptive designs and rely on strong assumptions. In this work, we propose selective randomization infer...
• Melanie Weber's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Weber, Melanie
  作者单位：Harvard University
• Augmented balancing weights as linear regression AI翻译
  作者：Bruns-Smith, David; Dukes, Oliver; Feller, Avi; Ogburn, Elizabeth L.
  作者单位：Stanford University; Ghent University; University of California System; University of California Berkeley; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
  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...
• Randomized empirical likelihood test for ultra-high dimensional means under general covariances AI翻译
  作者：Chen, Yuexin; Zhu, Lixing; Xu, Wangli
  作者单位：Renmin University of China; Renmin University of China; Beijing Normal University
  摘要：This article proposes a calibrated empirical likelihood test for ultra-high dimensional means that incorporates multiple projections. Under weak moment conditions on the distributions of data, we analyse all possible asymptotic distributions of the proposed test statistic in different scenarios. To determine the critical value and enhance test power, we employ the random symmetrization method based on the group of sign flips and use multiple selected projections. The test can still maintain th...