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Journal of the Royal Statistical Society Series B-Statistical Methodology最新文献

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Ruodu Wang's contribution to “Estimating means of bounded random variables by betting” by Ian Waudby-Smith and Aaditya Ramdas 王若度(Ruodu Wang)对Ian Waudby-Smith和Aaditya Ramdas合著的《通过投注估计有界随机变量的均值》的贡献
1区 数学 Q1 STATISTICS & PROBABILITY Pub Date : 2023-10-10 DOI: 10.1093/jrsssb/qkad110
Ruodu Wang
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引用次数: 0
Hien Nguyen's Contribution to the Discussion of “Estimating means of bounded random variables by betting” by Waudby-Smith and Ramdas Hien Nguyen对Waudby-Smith和Ramdas关于“通过投注估计有界随机变量的均值”的讨论的贡献
1区 数学 Q1 STATISTICS & PROBABILITY Pub Date : 2023-10-10 DOI: 10.1093/jrsssb/qkad121
Hien Nguyen
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引用次数: 0
Steven Howard’s contribution to the Discussion of “Estimating means of bounded random variables by betting” by Ian Waudby-Smith and Aaditya Ramdas Steven Howard对Ian Waudby-Smith和Aaditya Ramdas讨论的“通过投注估计有界随机变量的均值”的贡献
1区 数学 Q1 STATISTICS & PROBABILITY Pub Date : 2023-10-10 DOI: 10.1093/jrsssb/qkad114
Steven R Howard
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引用次数: 0
Anastasios N. Angelopoulos’ contribution to the Discussion of “Estimating means of bounded random variables by betting” by Ian Waudby-Smith and Aaditya Ramdas Anastasios N. Angelopoulos对Ian Waudby-Smith和Aaditya Ramdas讨论“通过投注估计有界随机变量的均值”的贡献
1区 数学 Q1 STATISTICS & PROBABILITY Pub Date : 2023-10-09 DOI: 10.1093/jrsssb/qkad115
Anastasios N Angelopoulos
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引用次数: 0
Vladimir Vovk's contribution to the Discussion of “Estimating means of bounded random variables by betting” by Ian Waudby-Smith and Aaditya Ramdas Vladimir Vovk对Ian Waudby-Smith和Aaditya Ramdas讨论“通过投注估计有界随机变量的均值”的贡献
1区 数学 Q1 STATISTICS & PROBABILITY Pub Date : 2023-10-09 DOI: 10.1093/jrsssb/qkad113
Vladimir Vovk
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引用次数: 0
Seconder of the vote of thanks to Waudy-Smith and Ramdas and contribution to the Discussion of “Estimating means of bounded random variables by betting” 其次,感谢Waudy-Smith和Ramdas对“通过投注估计有界随机变量的均值”的讨论所做的贡献
1区 数学 Q1 STATISTICS & PROBABILITY Pub Date : 2023-10-09 DOI: 10.1093/jrsssb/qkad123
Gergely Neu
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引用次数: 0
Rong Jiang and Keming Yu's Discussion of “Estimating means of bounded random variables by betting” by Ian Waudby-Smith and Aaditya Ramdas 蒋戎、余克明对Ian Waudby-Smith和Aaditya Ramdas的“用投注估计有界随机变量的均值”的讨论
1区 数学 Q1 STATISTICS & PROBABILITY Pub Date : 2023-10-09 DOI: 10.1093/jrsssb/qkad119
Rong Jiang, Keming Yu
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引用次数: 0
Johannes Ruf and Martin Larsson's Contribution to the Discussion of “Estimating means of bounded random variables by betting” by Ian Waudby-Smith and Aaditya Ramdas Johannes Ruf和Martin Larsson对Ian Waudby-Smith和Aaditya Ramdas讨论的“通过投注估计有界随机变量的均值”的贡献
1区 数学 Q1 STATISTICS & PROBABILITY Pub Date : 2023-10-09 DOI: 10.1093/jrsssb/qkad120
Martin Larsson, Johannes Ruf
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引用次数: 0
Ryan Martin's contribution to the Discussion of “Estimating means of bounded random variables by betting” by Ian Waudby-Smith and Aaditya Ramdas Ryan Martin对Ian Waudby-Smith和Aaditya Ramdas讨论的“通过投注估计有界随机变量的均值”的贡献
1区 数学 Q1 STATISTICS & PROBABILITY Pub Date : 2023-10-09 DOI: 10.1093/jrsssb/qkad112
Ryan Martin
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引用次数: 0
Root and community inference on latent network growth processes using noisy attachment models 基于噪声依恋模型的潜在网络生长过程的根和社区推断
1区 数学 Q1 STATISTICS & PROBABILITY Pub Date : 2023-09-26 DOI: 10.1093/jrsssb/qkad102
Harry Crane, Min Xu
Abstract Many existing statistical models for networks overlook the fact that most real-world networks are formed through a growth process. To address this, we introduce the PAPER (Preferential Attachment Plus Erdős-Rényi) model for random networks, where we let a random network G be the union of a preferential attachment (PA) tree T and additional Erdős-Rényi) (ER) random edges. The PA tree component captures the underlying growth/recruitment process of a network where vertices and edges are added sequentially, while the ER component can be regarded as random noise. Given only a single snapshot of the final network G, we study the problem of constructing confidence sets for the early history, in particular the root node, of the unobserved growth process; the root node can be patient zero in a disease infection network or the source of fake news in a social media network. We propose an inference algorithm based on Gibbs sampling that scales to networks with millions of nodes and provide theoretical analysis showing that the expected size of the confidence set is small so long as the noise level of the ER edges is not too large. We also propose variations of the model in which multiple growth processes occur simultaneously, reecting the growth of multiple communities, and we use these models to provide a new approach to community detection.
许多现有的网络统计模型忽略了一个事实,即大多数现实世界的网络都是通过一个增长过程形成的。为了解决这个问题,我们引入了随机网络的PAPER(优先附件加Erdős-Rényi)模型,其中我们让随机网络G是优先附件(PA)树T和其他Erdős-Rényi) (ER)随机边的并集。PA树组件捕获了一个网络的底层生长/招募过程,其中顶点和边是顺序添加的,而ER组件可以被视为随机噪声。给定最终网络G的单个快照,我们研究了为未观察到的增长过程的早期历史,特别是根节点构建置信集的问题;根节点可以是疾病感染网络中的零号病人,也可以是社交媒体网络中的假新闻来源。我们提出了一种基于Gibbs抽样的推理算法,该算法可扩展到具有数百万个节点的网络,并提供理论分析表明,只要ER边的噪声水平不太大,置信集的期望大小就很小。我们还提出了模型的变体,其中多个生长过程同时发生,反映了多个群落的生长,并使用这些模型提供了一种新的群落检测方法。
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引用次数: 0
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Journal of the Royal Statistical Society Series B-Statistical Methodology
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