2025

最新文章

• Ordinary differential equation models for a collection of discretized functions AI翻译
  作者：Shao, Lingxuan; Yao, Fang
  作者单位：Fudan University; Peking University
  摘要：The exploration of dynamic systems governed by ordinary differential equations (ODEs) holds great interest in the field of statistics. Existing research mainly focuses on a single function. This study generalizes the scope to analyse a collection of functions observed at discretized times, with sampling frequencies varying from sparse to dense designs. The range of ODE models studied caters to diverse dynamic systems, and includes the complex nonlinear and non-Lipschitz scenarios. We introduce...
• Conformal prediction with conditional guarantees AI翻译
  作者：Gibbs, Isaac; Cherian, John J.; Candes, Emmanuel J.
  作者单位：Stanford University; Stanford University
  摘要：We consider the problem of constructing distribution-free prediction sets with finite-sample conditional guarantees. Prior work has shown that it is impossible to provide exact conditional coverage universally in finite samples. Thus, most popular methods only guarantee marginal coverage over the covariates or are restricted to a limited set of conditional targets, e.g. coverage over a finite set of prespecified subgroups. This paper bridges this gap by defining a spectrum of problems that int...
• Robust angle-based transfer learning in high dimensions AI翻译
  作者：Gu, Tian; Han, Yi; Duan, Rui
  作者单位：Columbia University; Columbia University; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Transfer learning improves target model performance by leveraging data from related source populations, especially when target data are scarce. This study addresses the challenge of training high-dimensional regression models with limited target data in the presence of heterogeneous source populations. We focus on a practical setting where only parameter estimates of pretrained source models are available, rather than individual-level source data. For a single source model, we propose a novel ...
• Detection and inference of changes in high-dimensional linear regression with nonsparse structures AI翻译
  作者：Cho, Haeran; Kley, Tobias; Li, Housen
  作者单位：University of Bristol; University of Gottingen
  摘要：For data segmentation in high-dimensional linear regression settings, the regression parameters are often assumed to be exactly sparse segment-wise, which enables many existing methods to estimate the parameters locally via & ell;1-regularized maximum-likelihood-type estimation and then contrast them for change point detection. Contrary to this common practice, we show that the exact sparsity of neither regression parameters nor their differences, a.k.a. differential parameters, is necessary f...
• Semiparametric localized principal stratification analysis with continuous strata AI翻译
  作者：Zhang, Yichi; Yang, Shu
  作者单位：Indiana University System; Indiana University Bloomington; North Carolina State University
  摘要：Principal stratification is essential for revealing causal mechanisms involving post-treatment intermediate variables, in real-world applications like surrogate marker evaluation. Principal stratification analysis with continuous intermediate variables is increasingly common but challenging due to the infinite principal strata and the nonidentifiability and nonregularity of principal causal effects (PCEs). Inspired by recent research, we resolve these challenges by first using a flexible copul...
• Goodness-of-fit tests for high-dimensional Gaussian graphical models via exchangeable sampling AI翻译
  作者：Lin, Xiaotong; Li, Weihao; Tian, Fangqiao; Huang, Dongming
  作者单位：National University of Singapore; National University of Singapore
  摘要：We introduce a general framework for testing goodness-of-fit for Gaussian graphical models in both the low- and high-dimensional settings. This framework is based on a novel algorithm for generating exchangeable copies by conditioning on sufficient statistics. This framework provides exact finite-sample error control regardless of the dimension and allows flexible choices of test statistics to improve power. We explore several candidate test statistics and conduct extensive simulation studies ...
• Product centred Dirichlet processes for Bayesian multiview clustering AI翻译
  作者：Dombowsky, Alexander; Dunson, David B.
  作者单位：Duke University; Duke University
  摘要：While there is an immense literature on Bayesian methods for clustering, the multiview case has received little attention. This problem focuses on obtaining distinct but statistically dependent clusterings in a common set of entities for different data types. For example, clustering patients into subgroups with subgroup membership varying according to the domain of the patient variables. A challenge is how to model the across-view dependence between the partitions of patients into subgroups. T...
• Adaptive experiments toward learning treatment effect heterogeneity AI翻译
  作者：Wei, Waverly; Ma, Xinwei; Wang, Jingshen
  作者单位：University of Southern California; University of California System; University of California San Diego
  摘要：Understanding treatment effect heterogeneity has become an increasingly popular task in various fields, as it helps design personalized advertisements in e-commerce or targeted treatment in biomedical studies. However, most of the existing work in this research area focused on either analysing observational data based on strong causal assumptions or conducting post hoc analyses of randomized controlled trial data, and there has been limited effort dedicated to the design of randomized experime...
• 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...