Journal of the Royal Statistical Society: Series B

最新文章

• Two-phase rejective sampling and its asymptotic properties AI翻译
  作者：Yang, Shu; Ding, Peng
  作者单位：North Carolina State University; University of California System; University of California Berkeley
  摘要：Rejective sampling improves design and estimation efficiency of single-phase sampling when auxiliary information in a finite population is available. When such auxiliary information is unavailable, we propose to use two-phase rejective sampling (TPRS), which involves measuring auxiliary variables for the sample of units in the first phase, followed by the implementation of rejective sampling for the outcome in the second phase. We explore the asymptotic design properties of double expansion an...
• Online inference under over-parameterized models with hidden confounders AI翻译
  作者：Chen, Shuyan; Feng, Xingdong; Ge, Yeheng; Li, Tao; Wu, Mengyun
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Shanghai University of Finance & Economics; Hong Kong Polytechnic University
  摘要：In this paper, we study online estimation and inference of regression coefficients in the presence of hidden confounders by leveraging over-parameterized models. Unlike existing offline approaches that rely on factor and sparse models, our closed-form estimator simultaneously removes hidden-confounder bias and is directly applicable to streaming data. Using tools from random matrix theory, we analyse phase transition phenomena in the variance of the coefficient estimator that arise as the samp...
• Scalable Bayesian inference for heat kernel Gaussian processes on manifolds AI翻译
  作者：He, Junhui; Ma, Guoxuan; Kang, Jian; Yang, Ying
  作者单位：Tsinghua University; University of Michigan System; University of Michigan; Tsinghua University
  摘要：We establish a scalable manifold learning method and theory, motivated by the problem of estimating functional magnetic resonance imaging activation manifolds in the Human Connectome Project. Our primary contribution is the development of an efficient estimation technique for heat kernel Gaussian processes in the exponential family model. This approach handles large sample sizes n, preserves the intrinsic geometry of data, and significantly reduces computational complexity from O(n3) to O(n) v...
• Statistical exploration of the manifold hypothesis 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; University of Edinburgh; Heriot Watt University
  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...
• Minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy constraints AI翻译
  作者：Auddy, Arnab; Cai, T. Tony; Chakraborty, Abhinav
  作者单位：University System of Ohio; Ohio State University; University of Pennsylvania; Columbia University
  摘要：This paper considers minimax and adaptive transfer learning for nonparametric classification under the posterior drift model with distributed differential privacy constraints. Our study is conducted within a heterogeneous framework, encompassing diverse sample sizes, varying privacy parameters, and data heterogeneity across different servers. We first establish the minimax misclassification rate, precisely characterizing the effects of privacy constraints, source samples, and target samples on...
• Wasserstein generative regression AI翻译
  作者：Song, Shanshan; Wang, Tong; Shen, Guohao; Lin, Yuanyuan; Huang, Jian
  作者单位：Tongji University; Tongji University; Chinese University of Hong Kong; Hong Kong Polytechnic University; Hong Kong Polytechnic University; Hong Kong Polytechnic University
  摘要：In this paper, we propose a new and unified approach for nonparametric regression and conditional distribution learning. Our approach simultaneously estimates a regression function and a conditional generator using a generative learning framework, where a conditional generator is a function that can generate samples from a conditional distribution. The main idea is to estimate a conditional generator satisfying the constraint that it produces a good regression function estimator. We use deep n...
• KL-BSS: rethinking optimality for neighbourhood selection in structural equation models AI翻译
  作者：Gao, Ming; Tai, Wai Ming; Aragam, Bryon
  作者单位：University of Chicago
  摘要：We introduce a new method for neighbourhood selection in linear structural equation models that improves over classical methods such as best subset selection (BSS) and the Lasso. Our method, called KL-BSS, takes advantage of the existence of underlying structure in SEM-even when this structure is unknown-and is easily implemented using existing solvers. Under weaker eigenvalue conditions compared to BSS and the Lasso, KL-BSS can provably recover the support of linear models with fewer samples....
• 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...