Journal of the Royal Statistical Society Series B-Statistical Methodology最新文献_第5页

A fast asynchronous Markov chain Monte Carlo sampler for sparse Bayesian inference 稀疏贝叶斯推理的快速异步马尔可夫链蒙特卡罗采样器

1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-09-13 DOI: 10.1093/jrsssb/qkad078

Yves Atchadé, Liwei Wang

{"title":"A fast asynchronous Markov chain Monte Carlo sampler for sparse Bayesian inference","authors":"Yves Atchadé, Liwei Wang","doi":"10.1093/jrsssb/qkad078","DOIUrl":"https://doi.org/10.1093/jrsssb/qkad078","url":null,"abstract":"Abstract We propose a very fast approximate Markov chain Monte Carlo sampling framework that is applicable to a large class of sparse Bayesian inference problems. The computational cost per iteration in several regression models is of order O(n(s+J)), where n is the sample size, s is the underlying sparsity of the model, and J is the size of a randomly selected subset of regressors. This cost can be further reduced by data sub-sampling when stochastic gradient Langevin dynamics are employed. The algorithm is an extension of the asynchronous Gibbs sampler of Johnson et al. [(2013). Analyzing Hogwild parallel Gaussian Gibbs sampling. In Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS’13) (Vol. 2, pp. 2715–2723)], but can be viewed from a statistical perspective as a form of Bayesian iterated sure independent screening [Fan, J., Samworth, R., & Wu, Y. (2009). Ultrahigh dimensional feature selection: Beyond the linear model. Journal of Machine Learning Research, 10, 2013–2038]. We show that in high-dimensional linear regression problems, the Markov chain generated by the proposed algorithm admits an invariant distribution that recovers correctly the main signal with high probability under some statistical assumptions. Furthermore, we show that its mixing time is at most linear in the number of regressors. We illustrate the algorithm with several models.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135781780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stationary nonseparable space-time covariance functions on networks 网络上的平稳不可分时空协方差函数

1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-09-08 DOI: 10.1093/jrsssb/qkad082

Emilio Porcu, Philip A White, Marc G Genton

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引用次数: 0

Derandomised knockoffs: leveraging e-values for false discovery rate control 非随机仿冒品:利用e值控制错误发现率

1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-09-07 DOI: 10.1093/jrsssb/qkad085

Zhimei Ren, Rina Foygel Barber

{"title":"Derandomised knockoffs: leveraging <i>e</i>-values for false discovery rate control","authors":"Zhimei Ren, Rina Foygel Barber","doi":"10.1093/jrsssb/qkad085","DOIUrl":"https://doi.org/10.1093/jrsssb/qkad085","url":null,"abstract":"Abstract Model-X knockoffs is a flexible wrapper method for high-dimensional regression algorithms, which provides guaranteed control of the false discovery rate (FDR). Due to the randomness inherent to the method, different runs of model-X knockoffs on the same dataset often result in different sets of selected variables, which is undesirable in practice. In this article, we introduce a methodology for derandomising model-X knockoffs with provable FDR control. The key insight of our proposed method lies in the discovery that the knockoffs procedure is in essence an e-BH procedure. We make use of this connection and derandomise model-X knockoffs by aggregating the e-values resulting from multiple knockoff realisations. We prove that the derandomised procedure controls the FDR at the desired level, without any additional conditions (in contrast, previously proposed methods for derandomisation are not able to guarantee FDR control). The proposed method is evaluated with numerical experiments, where we find that the derandomised procedure achieves comparable power and dramatically decreased selection variability when compared with model-X knockoffs.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136364059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Seconder of the vote of thanks to Fong, Holmes and Walker and contribution to the Discussion of “Martingale Posterior Distributions” 其次，感谢Fong, Holmes和Walker对“鞅后验分布”的讨论所做的贡献

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-09-05 DOI: 10.1093/jrsssb/qkad087

Steffen Lauritzen

引用次数: 0

David Draper and Erdong Guo’s contribution to the Discussion of “Martingale Posterior Distributions” by Fong, Holmes and Walker David Draper和Erdong Guo对Fong, Holmes和Walker的“鞅后验分布”讨论的贡献

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-09-05 DOI: 10.1093/jrsssb/qkad091

David Draper, Erdong Guo

引用次数: 0

David Rossell’s contribution to the Discussion of “Martingale Posterior Distributions” by Fong, Holmes and Walker David Rossell对Fong, Holmes和Walker的“鞅后验分布”讨论的贡献

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-09-01 DOI: 10.1093/jrsssb/qkad096

David Rossell

引用次数: 0

Isadora Antoniano Villalobos’s contribution to the Discussion of “Martingale Posterior Distributions” by Fong, Holmes and Walker Isadora Antoniano Villalobos对Fong, Holmes和Walker的“鞅后分布”讨论的贡献

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-08-31 DOI: 10.1093/jrsssb/qkad089

Isadora Antoniano-Villalobos

引用次数: 0

Kolyan Ray and Botond Szabo’s contribution to the Discussion of “Martingale Posterior Distributions” by Fong, Holmes and Walke Kolyan Ray和Botond Szabo对Fong, Holmes和walker的“鞅后验分布”讨论的贡献

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-08-31 DOI: 10.1093/jrsssb/qkad098

Kolyan Ray, Botond Szabó

引用次数: 0

Ramsés H. Mena's Contribution to the Discussion of “Martingale posterior distributions” by Edwin Fong, Chris Holmes and Stephen G. Walker ramssamas H. Mena对“鞅后分布”讨论的贡献，作者:Edwin Fong, Chris Holmes和Stephen G. Walker

1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-08-29 DOI: 10.1093/jrsssb/qkad100

Ramsés H Mena

引用次数: 0

Priyantha Wijayatunga’s contribution to the Discussion of “Martingale Posterior Distributions” by Fong, Holmes and Walker Priyantha Wijayatunga对Fong, Holmes和Walker的“鞅后验分布”讨论的贡献

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-08-29 DOI: 10.1093/jrsssb/qkad099

Priyantha Wijayatunga

引用次数: 0