2026

最新文章

• Seconder of the vote of thanks to Bruns-Smith et al. and contribution to the Discussion of 'Augmented balancing weights as linear regression' AI翻译
  作者：Srakar, Andrej
  作者单位：Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; University of Ljubljana
• Oracle arrays and their use for constructing space-filling designs AI翻译
  作者：Tang, Boxin
  作者单位：Simon Fraser University
  摘要：The maximin distance is an attractive criterion for constructing space-filling designs. As factors in computer experiments are generally quantitative, the L-p-distance is appropriate. Theoretical construction of maximin L-p-distance designs, however, is extremely challenging. Given that directly attacking the problem is difficult, we propose an indirect approach that first constructs maximin Hamming distance designs and then constructs maximin L-p-distance designs using the former. The approac...
• Ian Gallagher's contribution to the Discussion of 'Statistical exploration of the manifold hypothesis' by Whiteley et al. AI翻译
  作者：Gallagher, Ian
  作者单位：University of Melbourne
• Multiple randomization designs: estimation and inference with interference AI翻译
  作者：Masoero, Lorenzo; Vijaykumar, Suhas; Richardson, Thomas S.; McQueen, James; Rosen, Ido; Burdick, Brian; Bajari, Pat; Imbens, Guido
  作者单位：Amazon.com; University of Washington; University of Washington Seattle; Stanford University; Stanford University
  摘要：Completely randomized experiments, originally developed by Fisher and Neyman in the 1930s, are still widely used in practice, even in online experimentation. However, such designs are of limited value for answering standard questions in marketplaces, where multiple populations of agents interact strategically, leading to complex patterns of spillover effects. In this article, we derive the finite-sample properties of tractable estimators for 'Simple Multiple Randomization Designs', a new class...
• The synthetic instrument: from sparse association to sparse causation AI翻译
  作者：Tang, Dingke; Kong, Dehan; Wang, Linbo
  作者单位：University of Ottawa; University of Toronto
  摘要：In many observational studies, researchers are often interested in the effects of multiple exposures on a single outcome. Standard approaches for high-dimensional data, such as the Lasso, assume that the associations between the exposures and the outcome are sparse. However, these methods do not estimate causal effects in the presence of unmeasured confounding. In this paper, we consider an alternative approach that assumes the causal effects under consideration are sparse. We show that under ...
• Statistical ranking with dynamic covariates AI翻译
  作者：Dong, Pinjun; Han, Ruijian; Jiang, Binyan; Xu, Yiming
  作者单位：Zhejiang University; Hong Kong Polytechnic University; University of Kentucky
  摘要：We introduce a general covariate-assisted statistical ranking model within the Plackett-Luce framework. Unlike previous studies that focus on individual effects with fixed covariates, our model allows covariates to vary across comparisons. This added flexibility enhances model fitting but also brings significant challenges in analysis. This article addresses these challenges in the context of maximum likelihood estimation (MLE). We first provide necessary and sufficient conditions for both mod...
• Structural classification of locally stationary time series based on second-order characteristics AI翻译
  作者：Qian, Chen; Ding, Xiucai; Li, Lexin
  作者单位：University of California System; University of California Davis; University of California System; University of California Berkeley
  摘要：Time series classification is crucial for numerous scientific and engineering applications. In this article, we present a numerically efficient, practically competitive, and theoretically rigorous classification method for distinguishing between two classes of locally stationary time series based on their time-domain, second-order characteristics. Our approach builds on the autoregressive approximation for locally stationary time series, imposes no requirement on the training sample size, and ...
• Maozai Tian, Shaopei Ma, Zhen Yu, and Yanan Hu's contribution to the Discussion of 'Statistical exploration of the manifold hypothesis' by Whiteley et al. AI翻译
  作者：Tian, Maozai; Ma, Shaopei; Yu, Zhen; Hu, Yanan
  作者单位：Renmin University of China; Xinjiang University of Finance & Economics; Changji University; University of International Business & Economics; University of International Business & Economics; Zhengzhou University
• Skew-symmetric approximations of posterior distributions AI翻译
  作者：Pozza, Francesco; Durante, Daniele; Szabo, Botond
  作者单位：Bocconi University; Bocconi University
  摘要：Popular deterministic approximations of posterior distributions from, e.g. the Laplace method, variational Bayes and expectation-propagation, generally rely on symmetric families, often taken to be Gaussian. This choice facilitates optimization and inference, but typically affects the quality of the approximation. In fact, even in basic parametric models, posterior distributions often display asymmetries that yield bias and reduced accuracy when considering symmetric approximations. Recent res...
• Using a two-parameter sensitivity analysis framework to efficiently combine randomized and nonrandomized studies AI翻译
  作者：Yu, Ruoqi; Karmakar, Bikram; Vandeleest, Jessica; Schwarz, Eleanor Bimla
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Wisconsin System; University of Wisconsin Madison; University of California System; University of California Davis; University of California System; University of California San Francisco
  摘要：Causal inference is vital for informed decision-making across fields such as biomedical research and social sciences. Randomized controlled trials (RCTs) are considered the gold standard for internal validity of inferences, whereas observational studies (OSs) often provide the opportunity for greater external validity. However, both data sources have inherent limitations preventing their use for broadly valid statistical inferences: RCTs may lack generalizability due to their selective eligibi...