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• Proposers of the vote of thanks to Crane and Xu and contribution to the Discussion of 'Root and community inference on the latent growth process of a network' AI翻译
  作者：Jog, Varun; Loh, Po-Ling
  作者单位：University of Cambridge
  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. Give...
• Tianxi Lis 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：Li, Tianxi
  作者单位：University of Minnesota System; University of Minnesota Twin Cities
  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. Give...
• 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...
• 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...
• 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
• 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...
• Fan Wang, Yi Yu and Alessandro Rinaldo'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：Wang, Fan; Yu, Yi; Rinaldo, Alessandro
  作者单位：University of Warwick; University of Texas System; University of Texas Austin
• Jason Wyse, James Ng, Arthur White and Michael Fop'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：Wyse, Jason; Ng, James; White, Arthur; Fop, Michael
  作者单位：Trinity College Dublin; University College Dublin
• Simultaneous false discovery proportion bounds via knockoffs and closed testing AI翻译
  作者：Li, Jinzhou; Maathuis, Marloes H.; Goeman, Jelle J.
  作者单位：Stanford University; Swiss Federal Institutes of Technology Domain; ETH Zurich; Leiden University; Leiden University Medical Center (LUMC); Leiden University - Excl LUMC
  摘要：We propose new methods to obtain simultaneous false discovery proportion bounds for knockoff-based approaches. We first investigate an approach based on Janson and Su's k-familywise error rate control method and interpolation. We then generalize it by considering a collection of k values, and show that the bound of Katsevich and Ramdas is a special case of this method and can be uniformly improved. Next, we further generalize the method by using closed testing with a multi-weighted-sum local t...