Journal of the Royal Statistical Society: Series B

最新文章

• Parameterizing and simulating from causal models (vol. 86, pg. 535, 2024) AI翻译
  作者：Evans, Robin J.; Didelez, Vanessa
• 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...
• Maozai Tian, Shuo Liu, and Tan Meng's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Tian, Maozai; Liu, Shuo; Meng, Tan
  作者单位：Renmin University of China; Xinjiang University of Finance & Economics; Changji University
• Principal stratification with U-statistics under principal ignorability AI翻译
  作者：Chen, Xinyuan; Li, Fan
  作者单位：Mississippi State University; Yale University; Yale University
  摘要：Principal stratification is a popular framework for causal inference in the presence of an intermediate outcome. While the principal average treatment effects are the standard target of inference, they may be insufficient when interest lies in the relative ordering of potential outcomes within a principal stratum. We introduce the principal generalized causal effect estimands to accommodate nonlinear contrast functions, providing robust, probability-scale summaries suitable for ordinal outcome...
• 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...