2026

最新文章

• Gaussianized design optimization for covariate balance in randomized experiments AI翻译
  作者：Guo, Wenxuan; Liang, Tengyuan; Toulis, Panos
  作者单位：University of Chicago
  摘要：Achieving covariate balance in randomized experiments enhances the precision of treatment effect estimation. However, existing methods often require heuristic adjustments based on domain knowledge and are primarily developed for binary treatments. This paper presents Gaussianized Design Optimization, a novel framework for optimally balancing covariates in experimental design. The core idea is to Gaussianize the treatment assignments: we model treatments as transformations of random variables d...
• Rocco Caprio and Adam M. Johansen's contribution to the Discussion of 'Statistical exploration of the manifold hypothesis' by Whiteley et al. AI翻译
  作者：Caprio, Rocco; Johansen, Adam M.
  作者单位：University of Warwick
• Mixing time of the conditional backward sampling particle filter AI翻译
  作者：Karjalainen, Joona; Lee, Anthony; Singh, Sumeetpal S.; Vihola, Matti
  作者单位：University of Jyvaskyla; University of Bristol; University of Wollongong
  摘要：The conditional backward sampling particle filter (CBPF) is a powerful Markov chain Monte Carlo sampler for general state space hidden Markov model (HMM) smoothing. It was proposed as an improvement over the conditional particle filter (CPF), which has an O(T2) complexity under a general 'strong' mixing assumption, where T is the time horizon. Empirical evidence of the superiority of the CBPF over the CPF has never been theoretically quantified. We show that the CBPF has O(TlogT) time complexi...
• Coloured Gaussian directed acyclic graphical models AI翻译
  作者：Boege, Tobias; Kubjas, Kaie; Misra, Pratik; Solus, Liam
  作者单位：UiT The Arctic University of Tromso; Aalto University; State University of New York (SUNY) System; Binghamton University, SUNY; Royal Institute of Technology
  摘要：We study submodels of Gaussian directed acyclic graph (DAG) models defined by partial homogeneity constraints imposed on the model error variances and structural coefficients. We represent these models with coloured DAGs and investigate their properties for use in statistical and causal inference. Local and global Markov properties are provided and shown to characterize the coloured DAG model. Additional properties relevant to causal discovery are studied, including the existence and nonexiste...
• Quantiles on global non-positive curvature spaces AI翻译
  作者：Shin, Ha-Young; Oh, Hee-Seok
  作者单位：Soongsil University; Soongsil University; Seoul National University (SNU)
  摘要：We propose a notion of geometric quantiles on Hadamard spaces, or global non-positive curvature spaces. After providing some definitions and basic properties, including scaled isometry equivariance and a necessary condition on the gradient of the quantile loss function, we investigate asymptotic properties, such as strong consistency and joint asymptotic normality. We provide a detailed description of how to compute quantiles using a gradient descent algorithm in hyperbolic space. We detail se...
• Asymptotic inference with flexible covariate adjustment under rerandomization and stratified rerandomization AI翻译
  作者：Wang, Bingkai; Li, Fan
  作者单位：University of Michigan System; University of Michigan; Yale University; Yale University
  摘要：Rerandomization is an effective treatment allocation procedure to control for baseline covariate imbalance. For estimating the average treatment effect, rerandomization has been previously shown to improve the precision of the unadjusted and the linearly adjusted estimators over simple randomization without compromising consistency. However, it remains unclear whether such results apply more generally to the class of M-estimators, including the g-computation formula with generalized linear reg...
• Nonparametric inference for censored data using deep neural networks AI翻译
  作者：Su, Wen; Wu, Qiang; Liu, Kin-Yat; Yin, Guosheng; Huang, Jian; Zhao, Xingqiu
  作者单位：City University of Hong Kong; Hong Kong Polytechnic University; Chinese University of Hong Kong; University of Hong Kong
  摘要：We propose a novel deep learning approach to nonparametric statistical inference for the conditional hazard function of survival time with right-censored data. We use a deep neural network (DNN) to approximate the logarithm of a conditional hazard function given covariates and obtain a DNN likelihood-based estimator of the conditional hazard function. Such an estimation approach enhances model flexibility and hence relaxes structural and functional assumptions on conditional hazard or survival...
• Thomas Maullin-Sapey's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Maullin-Sapey, Thomas
  作者单位：University of Bristol
• Multi-task learning for sparsity pattern heterogeneity: statistical and computational perspectives AI翻译
  作者：Behdin, Kayhan; Loewinger, Gabriel; Kishida, Kenneth T.; Parmigiani, Giovanni; Mazumder, Rahul
  作者单位：Massachusetts Institute of Technology (MIT); National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH); Wake Forest University; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Harvard University; Harvard T.H. Chan School of Public Health; Massachusetts Institute of Technology (MIT)
  摘要：We consider a problem in multi-task learning (MTL) where multiple linear models are jointly trained on a collection of datasets ('tasks'). A key novelty of our framework is that it allows the sparsity pattern of regression coefficients and the values of non-zero coefficients to differ across tasks while still leveraging partially shared structure. Our methods encourage models to share information across tasks through separately encouraging (1) coefficient supports, and/or (2) nonzero coefficie...
• Huber means on Riemannian manifolds AI翻译
  作者：Lee, Jongmin; Jung, Sungkyu
  作者单位：Pusan National University; Seoul National University (SNU); Seoul National University (SNU)
  摘要：This article introduces Huber means on Riemannian manifolds, providing a robust alternative to the Fr & eacute;chet mean by integrating elements of both L2 and L1 loss functions. The Huber means are designed to be highly resistant to outliers while maintaining efficiency, making it a valuable generalization of Huber's M-estimator for manifold-valued data. We comprehensively investigate the statistical and computational aspects of Huber means, demonstrating their utility in manifold-valued data...