Journal of the Royal Statistical Society: Series B

最新文章

• 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...
• 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...
• 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)
• Simon et al.'s contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Simon, Horst; Azz, Mohammed El-Amine; El Moukari, Roy; Porcu, Emilio
  作者单位：Khalifa University of Science & Technology
• Yanbo Tang's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Tang, Yanbo
  作者单位：Imperial College London
• Fukang Zhu and Xiangyu Guo's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Zhu, Fukang; Guo, Xiangyu
  作者单位：Jilin University; Hebei University of Economics & Business