2026

最新文章

• Cross-validation with antithetic Gaussian randomization AI翻译
  作者：Liu, Sifan; Panigrahi, Snigdha; Soloff, Jake A.
  作者单位：Duke University; University of Michigan System; University of Michigan
  摘要：We introduce a new cross-validation (CV) method based on an equicorrelated Gaussian randomization scheme. Our method is well-suited for problems where sample splitting is infeasible, either because the data violate the assumption of independent and identically distributed samples, or because there are insufficient samples to form representative train-test data pairs. In such problems, our method provides a simple, principled, and computationally efficient approach to estimating prediction erro...
• Autoregressive networks with dependent edges AI翻译
  作者：Chang, Jinyuan; Fang, Qin; Kolaczyk, Eric D.; MacDonald, Peter W.; Yao, Qiwei
  作者单位：Southwestern University of Finance & Economics - China; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University of Sydney; McGill University; University of Waterloo; University of London; London School Economics & Political Science
  摘要：We propose an autoregressive framework for modelling dynamic networks with dependent edges. It encompasses models that accommodate, for example, transitivity, degree heterogenenity, and other stylized features often observed in real network data. By assuming the edges of networks at each time are independent conditionally on their lagged values, the models, which exhibit a close connection with temporal exponential random graph models, facilitate both simulation and the maximum likelihood esti...
• Wanjie Wang's contribution to the Discussion of 'Statistical exploration of the manifold hypothesis' by Whiteley et al. AI翻译
  作者：Wang, Wanjie
  作者单位：National University of Singapore
• Inference on function-valued parameters using a restricted score test AI翻译
  作者：Hudson, Aaron; Carone, Marco; Shojaie, Ali
  作者单位：Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle
  摘要：It is often of interest to make inference on an unknown function that is a local parameter of the data-generating mechanism, such as a density or regression function. Such estimands can typically only be estimated at a slower-than-parametric rate in nonparametric and semiparametric models, and performing calibrated inference can be challenging. In many cases, these estimands can be expressed as the minimizer of a population risk functional. Here, we propose a general framework that leverages s...
• ART: distribution-free and model-agnostic changepoint detection with finite-sample guarantees AI翻译
  作者：Cui, Xiaolong; Geng, Haoyu; Wang, Guanghui; Wang, Zhaojun; Zou, Changliang
  作者单位：Nankai University; Nankai University
  摘要：We introduce ART, a distribution-free and model-agnostic framework for changepoint analysis with finite-sample guarantees. ART transforms independent observations into real-valued scores via a symmetric function; under the null hypothesis of no changepoint these scores are exchangeable. Ranking and aggregating the scores yields test statistics whose null distribution is known exactly from the permutation law of ranks, enabling exact finite-sample Type I error control without repeated refitting...
• Root cause discovery via permutations and Cholesky decomposition AI翻译
  作者：Li, Jinzhou; Chu, Benjamin B.; Scheller, Ines F.; Gagneur, Julien; Maathuis, Marloes H.
  作者单位：National University of Singapore; Stanford University; Technical University of Munich; Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Technical University of Munich; Technical University of Munich; Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：This work is motivated by the following problem: Can we identify the disease-causing gene in a patient affected by a monogenic disorder? This problem is an instance of root cause discovery. Specifically, we aim to identify the intervened variable in one interventional sample using a set of observational samples as reference. We consider a linear structural equation model where the causal ordering is unknown. We begin by examining a simple method that uses squared z-scores and characterize the ...
• Efficient nonparametric estimators of discrimination measures with censored survival data AI翻译
  作者：Breum, Marie Skov; Martinussen, Torben
  作者单位：University of Copenhagen
  摘要：Discrimination measures such as the concordance index and the cumulative-dynamic time-dependent area under the ROC-curve are widely used in the medical literature for evaluating the predictive accuracy of a scoring rule which relates a set of prognostic markers to the risk of experiencing a particular event. Often the scoring rule being evaluated in terms of discriminatory ability is the linear predictor of a survival regression model such as the Cox proportional hazards model. This has the un...
• A copula graphical model for multi-attribute data using optimal transport AI翻译
  作者：Zhang, Qi; Li, Bing; Xue, Lingzhou
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：Motivated by modern data types such as images and multi-view data, the multi-attribute graphical model aims to uncover conditional independence structures among vector-valued nodes. Under the Gaussian assumption, such independence is encoded in blockwise zeros of the precision matrix. To relax the restrictive Gaussian assumption, we propose a semiparametric multi-attribute graphical model leveraging a newly introduced cyclically monotone copula. This copula treats the distribution of node vect...
• Byzantine-tolerant distributed learning of finite mixture models AI翻译
  作者：Zhang, Qiong; Tan, Yan Shuo; Chen, Jiahua
  作者单位：Renmin University of China; National University of Singapore; University of British Columbia
  摘要：Traditional statistical methods need to be updated to work with modern distributed data storage paradigms. The split-and-conquer framework that learns models on local machines and averaging their parameter estimates is common. However, this does not work for the important problem of learning finite mixture models, because subpopulation indices on each local machine may be arbitrarily permuted (the 'label switching problem'). Earlier work proposed mixture reduction (MR) to address this issue, o...
• Seconder of the vote of thanks to Whiteley et al. and Contribution to the Discussion of 'Statistical exploration of the manifold hypothesis' AI翻译
  作者：Aamari, Eddie
  作者单位：Centre National de la Recherche Scientifique (CNRS); Universite PSL; Ecole Normale Superieure (ENS)