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

Strategic two-sample test via the two-armed bandit process 通过双臂盗匪过程进行战略双样本检验

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-06-14 DOI: 10.1093/jrsssb/qkad061

Zengjing Chen, Xiaodong Yan, Guodong Zhang

引用次数: 0

Quasi-Newton updating for large-scale distributed learning 大规模分布式学习的准牛顿更新

1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-06-10 DOI: 10.1093/jrsssb/qkad059

Shuyuan Wu, Danyang Huang, Hansheng Wang

引用次数: 0

Correction to: Autoregressive optimal transport models. 更正：自回归最优运输模型。

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-05-31 eCollection Date: 2023-07-01 DOI: 10.1093/jrsssb/qkad057

引用次数: 0

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

{"title":"Non-parametric inference about mean functionals of non-ignorable non-response data without identifying the joint distribution.","authors":"Wei Li, Wang Miao, Eric Tchetgen Tchetgen","doi":"10.1093/jrsssb/qkad047","DOIUrl":"10.1093/jrsssb/qkad047","url":null,"abstract":"<p><p>We consider identification and inference about mean functionals of observed covariates and an outcome variable subject to non-ignorable missingness. By leveraging a shadow variable, we establish a necessary and sufficient condition for identification of the mean functional even if the full data distribution is not identified. We further characterize a necessary condition for <math><msqrt><mi>n</mi></msqrt></math>-estimability of the mean functional. This condition naturally strengthens the identifying condition, and it requires the existence of a function as a solution to a representer equation that connects the shadow variable to the mean functional. Solutions to the representer equation may not be unique, which presents substantial challenges for non-parametric estimation, and standard theories for non-parametric sieve estimators are not applicable here. We construct a consistent estimator of the solution set and then adapt the theory of extremum estimators to find from the estimated set a consistent estimator of an appropriately chosen solution. The estimator is asymptotically normal, locally efficient and attains the semi-parametric efficiency bound under certain regularity conditions. We illustrate the proposed approach via simulations and a real data application on home pricing.</p>","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":"85 3","pages":"913-935"},"PeriodicalIF":3.1,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376447/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9964441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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