Journal of the Royal Statistical Society: Series B

最新文章

• On the cross-validation bias due to unsupervised preprocessing AI翻译
  作者：Moscovich, Amit; Rosset, Saharon
  作者单位：Tel Aviv University
  摘要：Cross-validation is the de facto standard for predictive model evaluation and selection. In proper use, it provides an unbiased estimate of a model's predictive performance. However, data sets often undergo various forms of data-dependent preprocessing, such as mean-centring, rescaling, dimensionality reduction and outlier removal. It is often believed that such preprocessing stages, if done in an unsupervised manner (that does not incorporate the class labels or response values) are generally...
• A statistical interpretation of spectral embedding: The generalised random dot product graph AI翻译
  作者：Rubin-Delanchy, Patrick; Cape, Joshua; Tang, Minh; Priebe, Carey E.
  作者单位：University of Bristol; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; North Carolina State University; Johns Hopkins University
  摘要：Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph. This paper proposes a generalisation of the latent position network model known as the random dot product graph, to allow interpretation of those vector representations as latent position estimates. The generalisation is needed to model heterophilic connectivity (e.g. 'opposites attract') and to cope with negative eigenvalues more generally. We show that, whether the adjacency or norma...
• Seconder of the vote of thanks to Dong et al. and contribution to the Discussion of 'Gaussian Differential Privacy' AI翻译
  作者：Avella-Medina, Marco
  作者单位：Columbia University
• Authors' reply to the Discussion of 'Gaussian Differential Privacy' by Dong et al AI翻译
  作者：Dong, Jinshuo; Roth, Aaron; Su, Weijie J.
  作者单位：University of Pennsylvania
• High-dimensional changepoint estimation with heterogeneous missingness AI翻译
  作者：Follain, Bertille; Wang, Tengyao; Samworth, Richard J.
  作者单位：University of Cambridge; Inria; Universite PSL; Ecole Normale Superieure (ENS); University of London; London School Economics & Political Science; University of London; University College London
  摘要：We propose a new method for changepoint estimation in partially observed, high-dimensional time series that undergo a simultaneous change in mean in a sparse subset of coordinates. Our first methodological contribution is to introduce a 'MissCUSUM' transformation (a generalisation of the popular cumulative sum statistics), that captures the interaction between the signal strength and the level of missingness in each coordinate. In order to borrow strength across the coordinates, we propose to ...
• Elizabeth L Ogburn, Junhui Cai, Arun K Kuchibhotla, Richard A Berk and Andreas Buja's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Ogburn, Elizabeth L.; Cai, Junhui; Kuchibhotla, Arun K.; Berk, Richard A.; Buja, Andreas
  作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; University of Pennsylvania; Carnegie Mellon University; University of Pennsylvania; Simons Foundation; Flatiron Institute
• Functional peaks-over-threshold analysis AI翻译
  作者：de Fondeville, Raphael; Davison, Anthony C.
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
  摘要：Peaks-over-threshold analysis using the generalised Pareto distribution is widely applied in modelling tails of univariate random variables, but much information may be lost when complex extreme events are studied using univariate results. In this paper, we extend peaks-over-threshold analysis to extremes of functional data. Threshold exceedances defined using a functional r are modelled by the generalised r-Pareto process, a functional generalisation of the generalised Pareto distribution tha...
• Optimal and maximin procedures for multiple testing problems AI翻译
  作者：Rosset, Saharon; Heller, Ruth; Painsky, Amichai; Aharoni, Ehud
  作者单位：Tel Aviv University; Tel Aviv University; International Business Machines (IBM); IBM ISRAEL
  摘要：Multiple testing problems (MTPs) are a staple of modern statistical analysis. The fundamental objective of MTPs is to reject as many false null hypotheses as possible (that is, maximize some notion of power), subject to controlling an overall measure of false discovery, like family-wise error rate (FWER) or false discovery rate (FDR). In this paper we provide generalizations to MTPs of the optimal Neyman-Pearson test for a single hypothesis. We show that for simple hypotheses, for both FWER an...
• Anna Choi and Weng Kee Wong's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Choi, Anna; Wong, Weng Kee
  作者单位：Stanford University; University of California System; University of California Los Angeles
• Bootstrap inference for the finite population mean under complex sampling designs AI翻译
  作者：Wang, Zhonglei; Peng, Liuhua; Kim, Jae Kwang
  作者单位：Xiamen University; Xiamen University; University of Melbourne; Iowa State University
  摘要：Bootstrap is a useful computational tool for statistical inference, but it may lead to erroneous analysis under complex survey sampling. In this paper, we propose a unified bootstrap method for stratified multi-stage cluster sampling, Poisson sampling, simple random sampling without replacement and probability proportional to size sampling with replacement. In the proposed bootstrap method, we first generate bootstrap finite populations, apply the same sampling design to each bootstrap populat...