Journal of the Royal Statistical Society Series B-Statistical Methodology最新文献_第8页

Alexander Van Werde's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng Alexander Van Werde对Rohe的“Vintage Factor Analysis with variimax perform Statistical Inference”讨论的贡献曾。

1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-05-25 DOI: 10.1093/jrsssb/qkad035

Alexander Van Werde

引用次数: 0

Konstantin Siroki and Korbinian Strimmer’s contribution to the Discussion of “Vintage Factor Analysis with Varimax Performs Statistical Inference” by Rohe & Zeng Konstantin Siroki和Korbinian Strimmer对Rohe & Zeng的“Vintage Factor Analysis with variimax演出Statistical Inference”讨论的贡献

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-05-23 DOI: 10.1093/jrsssb/qkad055

Konstantin Siroki, K. Strimmer

引用次数: 0

Florian Pargent, David Goretzko and Timo von Oertzen’s contribution to the Discussion of “Vintage Factor Analysis with Varimax Performs Statistical Inference” by Rohe & Zeng Florian Pargent, David Goretzko和Timo von Oertzen对Rohe & Zeng的“Vintage Factor Analysis with variimax执行统计推断”讨论的贡献

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-05-23 DOI: 10.1093/jrsssb/qkad054

F. Pargent, D. Goretzko, Timo von Oertzen

引用次数: 1

Correction to: Ordering factorial experiments 修正:排序阶乘实验

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-05-16 DOI: 10.1093/jrsssb/qkad053

引用次数: 0

A model where the least trimmed squares estimator is maximum likelihood 最小二乘估计量为最大似然的一种模型

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-05-15 DOI: 10.1093/jrsssb/qkad028

Vanessa Berenguer-Rico, S. Johansen, B. Nielsen

引用次数: 2

Non-parametric inference about mean functionals of non-ignorable non-response data without identifying the joint distribution. 在不确定联合分布的情况下，对不可忽略的非响应数据的平均函数进行非参数推断。

IF 3.1 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-05-08 eCollection Date: 2023-07-01 DOI: 10.1093/jrsssb/qkad047

Wei Li, Wang Miao, Eric Tchetgen Tchetgen

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引用次数: 0

Sparse Kronecker product decomposition: a general framework of signal region detection in image regression 稀疏Kronecker积分解:图像回归中信号区域检测的一般框架

1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-04-27 DOI: 10.1093/jrsssb/qkad024

Sanyou Wu, Long Feng

{"title":"Sparse Kronecker product decomposition: a general framework of signal region detection in image regression","authors":"Sanyou Wu, Long Feng","doi":"10.1093/jrsssb/qkad024","DOIUrl":"https://doi.org/10.1093/jrsssb/qkad024","url":null,"abstract":"Abstract This paper aims to present the first Frequentist framework on signal region detection in high-resolution and high-order image regression problems. Image data and scalar-on-image regression are intensively studied in recent years. However, most existing studies on such topics focussed on outcome prediction, while the research on region detection is rather limited, even though the latter is often more important. In this paper, we develop a general framework named Sparse Kronecker Product Decomposition (SKPD) to tackle this issue. The SKPD framework is general in the sense that it works for both matrices and tensors represented image data. Our framework includes one-term, multi-term, and nonlinear SKPDs. We propose nonconvex optimization problems for one-term and multi-term SKPDs and develop path-following algorithms for the nonconvex optimization. Under a Restricted Isometric Property, the computed solutions of the path-following algorithm are guaranteed to converge to the truth with a particularly chosen initialization even though the optimization is nonconvex. Moreover, the region detection consistency could also be guaranteed. The nonlinear SKPD is highly connected to shallow convolutional neural networks (CNN), particularly to CNN with one convolutional layer and one fully-connected layer. Effectiveness of SKPD is validated by real brain imaging data in the UK Biobank database.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136223251","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

Manifold lifting: scaling Markov chain Monte Carlo to the vanishing noise regime 流形提升:缩放马尔可夫链蒙特卡洛到消失的噪声状态

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-04-24 DOI: 10.1093/jrsssb/qkad023

K. Au, Matthew M. Graham, Alexandre Hoang Thiery

{"title":"Manifold lifting: scaling Markov chain Monte Carlo to the vanishing noise regime","authors":"K. Au, Matthew M. Graham, Alexandre Hoang Thiery","doi":"10.1093/jrsssb/qkad023","DOIUrl":"https://doi.org/10.1093/jrsssb/qkad023","url":null,"abstract":"\u0000 Standard Markov chain Monte Carlo methods struggle to explore distributions that concentrate in the neighbourhood of low-dimensional submanifolds. This pathology naturally occurs in Bayesian inference settings when there is a high signal-to-noise ratio in the observational data but the model is inherently over-parametrised or nonidentifiable. In this paper, we propose a strategy that transforms the original sampling problem into the task of exploring a distribution supported on a manifold embedded in a higher-dimensional space; in contrast to the original posterior this lifted distribution remains diffuse in the limit of vanishing observation noise. We employ a constrained Hamiltonian Monte Carlo method, which exploits the geometry of this lifted distribution, to perform efficient approximate inference. We demonstrate in numerical experiments that, contrarily to competing approaches, the sampling efficiency of our proposed methodology does not degenerate as the target distribution to be explored concentrates near low-dimensional submanifolds. Python code reproducing the results is available at https://doi.org/10.5281/zenodo.6551654.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":"33 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74984921","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: 1

Ordering factorial experiments 排序阶乘实验

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-04-20 DOI: 10.1093/jrsssb/qkad027

Liuqing Yang, Yongdao Zhou, Min-Qian Liu

引用次数: 0

Peter J Bickel, Derek Bean, Aiyou Chen and Purnamrita Sarkar’s contribution to the Discussion of “Vintage Factor Analysis with Varimax Performs Statistical Inference” by Rohe & Zeng Peter J Bickel, Derek Bean, Aiyou Chen和Purnamrita Sarkar对Rohe & Zeng的“Vintage Factor Analysis with Varimax执行统计推断”讨论的贡献

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-04-12 DOI: 10.1093/jrsssb/qkad037

引用次数: 0