Journal of the Royal Statistical Society: Series B

最新文章

• Testing many constraints in possibly irregular models using incomplete U-statistics AI翻译
  作者：Sturma, Nils; Drton, Mathias; Leung, Dennis
  作者单位：Technical University of Munich; Technical University of Munich; University of Melbourne; Technical University of Munich
  摘要：We consider the problem of testing a null hypothesis defined by equality and inequality constraints on a statistical parameter. Testing such hypotheses can be challenging because the number of relevant constraints may be on the same order or even larger than the number of observed samples. Moreover, standard distributional approximations may be invalid due to irregularities in the null hypothesis. We propose a general testing methodology that aims to circumvent these difficulties. The constrai...
• A. Philip Dawid's contribution to the Discussion of 'Parameterizing and simulating from causal models' by Evans and Didelez AI翻译
  作者：Dawid, A. Philip
  作者单位：University of Cambridge
• Full-model estimation for non-parametric multivariate finite mixture models AI翻译
  作者：de Chaumaray, Marie Du Roy; Marbac, Matthieu
  作者单位：Universite de Rennes; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Centre National de la Recherche Scientifique (CNRS); Universite de Rennes; Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite de Rennes
  摘要：This paper addresses the problem of full-model estimation for non-parametric finite mixture models. It presents an approach for selecting the number of components and the subset of discriminative variables (i.e. the subset of variables having different distributions among the mixture components) by considering an upper bound on the number of components (this number being allowed to increase with the sample size). The proposed approach considers a discretization of each variable into B bins and...
• Holdout predictive checks for Bayesian model criticism AI翻译
  作者：Moran, Gemma E.; Blei, David M.; Ranganath, Rajesh
  作者单位：Rutgers University System; Rutgers University New Brunswick; Columbia University; New York University; Rutgers University System; Rutgers University New Brunswick
  摘要：Bayesian modelling helps applied researchers to articulate assumptions about their data and develop models tailored for specific applications. Thanks to good methods for approximate posterior inference, researchers can now easily build, use, and revise complicated Bayesian models for large and rich data. These capabilities, however, bring into focus the problem of model criticism. Researchers need tools to diagnose the fitness of their models, to understand where they fall short, and to guide ...
• Marta Catalano, Augusto Fasano, Matteo Giordano, and Giovanni Rebaudo's contribution to the Discussion of 'Root and community inference on the latent growth process of a network' by Crane and Xu AI翻译
  作者：Catalano, Marta; Fasano, Augusto; Giordano, Matteo; Rebaudo, Giovanni
  作者单位：Luiss Guido Carli University; Catholic University of the Sacred Heart; University of Turin
• Covariate-adaptive randomization inference in matched designs AI翻译
  作者：Pimentel, Samuel D.; Huang, Yaxuan
  作者单位：University of California System; University of California Berkeley
  摘要：It is common to conduct causal inference in matched observational studies by proceeding as though treatment assignments within matched sets are assigned uniformly at random and using this distribution as the basis for inference. This approach ignores observed discrepancies in matched sets that may be consequential for the distribution of treatment, which are succinctly captured by within-set differences in the propensity score. We address this problem via covariate-adaptive randomization infer...
• Optimal and Maximin Procedures for Multiple Testing Problems (vol 84, pg 1105, 2022) 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
• Root and community inference on the latent growth process of a network AI翻译
  作者：Feng, Yang; Sun, Jiajin
  作者单位：New York University; Columbia University
  摘要：Many statistical models for networks overlook the fact that most real-world networks are formed through a growth process. To address this, we introduce the Preferential Attachment Plus Erdos-Renyi model, where we let a random network G be the union of a preferential attachment (PA) tree T and additional Erdos- Renyi (ER) random edges. The PA tree captures the underlying growth process of a network where vertices/edges are added sequentially, while the ER component can be regarded as noise. Giv...
• From denoising diffusions to denoising Markov models AI翻译
  作者：Benton, Joe; Shi, Yuyang; De Bortoli, Valentin; Deligiannidis, George; Doucet, Arnaud
  作者单位：University of Oxford; Universite PSL; Ecole Normale Superieure (ENS)
  摘要：Denoising diffusions are state-of-the-art generative models exhibiting remarkable empirical performance. They work by diffusing the data distribution into a Gaussian distribution and then learning to reverse this noising process to obtain synthetic datapoints. The denoising diffusion relies on approximations of the logarithmic derivatives of the noised data densities using score matching. Such models can also be used to perform approximate posterior simulation when one can only sample from the...
• Yingnian Wu and Weng Kee Wong's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Wu, Ying Nian; Wong, Weng Kee
  作者单位：University of California System; University of California Los Angeles; University of California System; University of California Los Angeles