Journal of the Royal Statistical Society: Series B

最新文章

• Online multivariate changepoint detection: leveraging links with computational geometry AI翻译
  作者：Pishchagina, Liudmila; Romano, Gaetano; Fearnhead, Paul; Runge, Vincent; Rigaill, Guillem
  作者单位：Centre National de la Recherche Scientifique (CNRS); Universite Paris Saclay; Lancaster University; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); INRAE; Universite Paris Cite; INRAE; Universite Paris Saclay; AgroParisTech
  摘要：The increasing volume of data streams poses significant computational challenges for detecting changepoints online. Likelihood-based methods are effective, but a naive sequential implementation becomes impractical online due to high computational costs. We develop an online algorithm that exactly calculates the likelihood ratio test for a single changepoint in p-dimensional data streams by leveraging a fascinating connection with computational geometry. This connection straightforwardly allows...
• Improving the false coverage rate adjusted confidence intervals AI翻译
  作者：Frostig, Tzviel; Benjamini, Yoav
  作者单位：Tel Aviv University
  摘要：This study addresses the challenges of inference following selection in fields like clinical trials, genome-wide association studies, and functional magnetic resonance imaging, where traditional methods like simultaneous confidence intervals (CIs) might be too conservative. We introduce an improved false coverage-statement rate controlling CIs, when the selection is done by passing a threshold in a certain direction. The CIs for the selected parameters are similar to those proposed by Benjamin...
• A general framework for cutting feedback within modularized Bayesian inference AI翻译
  作者：Liu, Yang; Goudie, Robert J. B.
  作者单位：University of Cambridge; MRC Biostatistics Unit
  摘要：Standard Bayesian inference enables building models that combine information from various sources, but this inference may not be reliable if components of the model are misspecified. Cut inference, a particular type of modularized Bayesian inference, is an alternative that splits a model into modules and cuts the feedback from any suspect module. Previous studies have focused on a two module case, but a more general definition of a 'module' remains unclear. We present a formal definition of a ...
• Sequential model confidence sets AI翻译
  作者：Arnold, Sebastian; Gavrilopoulos, Georgios; Schulz, Benedikt; Ziegel, Johanna
  作者单位：Centrum Wiskunde & Informatica (CWI); Swiss Federal Institutes of Technology Domain; ETH Zurich; Helmholtz Association; Karlsruhe Institute of Technology
  摘要：In most prediction and estimation situations, scientists consider various statistical models for the same problem, and naturally want to select amongst the best. Hansen et al. [(2011). The model confidence set. Econometrica: Journal of the Econometric Society, 79(2), 453-497] provide a powerful solution to this problem by the so-called model confidence set, a subset of the original set of available models that contains the best models with a given level of confidence. Importantly, model confid...
• Statistical inference for Gaussian Whittle-Matérn fields on metric graphs AI翻译
  作者：Bolin, David; Simas, Alexandre B.; Wallin, Jonas
  作者单位：King Abdullah University of Science & Technology; Lund University
  摘要：Whittle-Mat & eacute;rn fields are a recently introduced class of Gaussian processes on metric graphs, specified as solutions to a fractional-order stochastic differential equation. Unlike previous covariance-based methods, these fields are well-defined for any compact metric graph and can provide Gaussian processes with differentiable sample paths. We derive the main statistical properties, including the consistency and asymptotic normality of maximum likelihood estimators and the necessary a...
• Safaa K. Kadhem's contribution to the Discussion on 'Statistical exploration of the manifold hypothesis' by Nick Whiteley, Annie Grayb, and Patrick Rubin-Delanchy AI翻译
  作者：Kadhem, Safaa K.
• Poorbita Kundu's and Johannes Schmidt-Hieber's contribution to the Discussion of 'Statistical exploration of the manifold hypothesis' by Whiteley et al. AI翻译
  作者：Kundu, Poorbita; Schmidt-Hieber, Johannes
  作者单位：Fred Hutchinson Cancer Center; University of Twente
• A new integrative learning framework for integrating multiple secondary outcomes into primary outcome analysis: a case study on liver health AI翻译
  作者：Deng, Daxuan; Han, Peisong; Chen, Shuo; Wang, Ming; Chen, Chixiang
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Penn State Health; Gilead Sciences; University System of Maryland; University of Maryland Baltimore; University System of Ohio; Case Western Reserve University
  摘要：In the era of big data, secondary outcomes have become increasingly important alongside primary outcomes. These secondary outcomes, which can be derived from traditional endpoints in clinical trials, compound measures, or risk prediction scores, hold the potential to enhance the analysis of primary outcomes. Our method is motivated by the challenge of utilizing multiple secondary outcomes, such as blood biochemistry markers and urine assays, to improve the analysis of the primary outcome relat...
• SymmPI: predictive inference for data with group symmetries AI翻译
  作者：Dobriban, Edgar; Yu, Mengxin
  作者单位：University of Pennsylvania; Washington University (WUSTL)
  摘要：Quantifying the uncertainty of predictions is a core problem in modern statistics. Methods for predictive inference have been developed under a variety of assumptions, often-for instance, in standard conformal prediction-relying on the invariance of the distribution of the data under special groups of transformations such as permutation groups. Moreover, many existing methods for predictive inference aim to predict unobserved outcomes in sequences of feature-outcome observations. Meanwhile, th...
• Recursive learning without collapse: a weighting-based stabilization framework AI翻译
  作者：He, Hengzhi; Xu, Shirong; Cheng, Guang
  作者单位：University of California System; University of California Los Angeles; Xiamen University
  摘要：Recent studies identified an intriguing phenomenon in recursive generative model training known as model collapse, where models trained on data generated by previous models exhibit severe performance degradation. Addressing this issue and developing more effective training strategies have become central challenges in generative model research. In this paper, we investigate this phenomenon within a novel framework, where generative models are iteratively trained on a combination of newly collec...