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Testing the number of common factors by bootstrapped sample covariance matrix in high-dimensional factor models 在高维因子模型中通过引导样本协方差矩阵检验公共因子的数量
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-04-22 DOI: 10.1080/01621459.2024.2346364
Long Yu, Peng Zhao, Wang Zhou
{"title":"Testing the number of common factors by bootstrapped sample covariance matrix in high-dimensional factor models","authors":"Long Yu, Peng Zhao, Wang Zhou","doi":"10.1080/01621459.2024.2346364","DOIUrl":"https://doi.org/10.1080/01621459.2024.2346364","url":null,"abstract":"","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140675239","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
Estimating trans-ancestry genetic correlation with unbalanced data resources 利用不平衡数据资源估算跨宗族遗传相关性
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-04-19 DOI: 10.1080/01621459.2024.2344703
Bingxin Zhao, Xiaochen Yang, Hongtu Zhu
{"title":"Estimating trans-ancestry genetic correlation with unbalanced data resources","authors":"Bingxin Zhao, Xiaochen Yang, Hongtu Zhu","doi":"10.1080/01621459.2024.2344703","DOIUrl":"https://doi.org/10.1080/01621459.2024.2344703","url":null,"abstract":"The aim of this paper is to propose a novel method for estimating trans-ancestry genetic correlations in genome-wide association studies (GWAS) using genetically-predicted observations. These corre...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140620558","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
Modeling and Learning on High-Dimensional Matrix-Variate Sequences 高维矩阵变量序列的建模与学习
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-04-19 DOI: 10.1080/01621459.2024.2344687
Xu Zhang, Catherine C. Liu, Jianhua Guo, K. C. Yuen, A. H. Welsh
{"title":"Modeling and Learning on High-Dimensional Matrix-Variate Sequences","authors":"Xu Zhang, Catherine C. Liu, Jianhua Guo, K. C. Yuen, A. H. Welsh","doi":"10.1080/01621459.2024.2344687","DOIUrl":"https://doi.org/10.1080/01621459.2024.2344687","url":null,"abstract":"We propose a new matrix factor model, named RaDFaM, which is strictly derived based on the general rank decomposition and assumes a structure of a high-dimensional vector factor model for each basi...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140620560","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
Statistical Methods in Health Disparity Research. 健康差异研究中的统计方法》。
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-04-17 DOI: 10.1080/01621459.2024.2344715
Susan M. Paddock
{"title":"Statistical Methods in Health Disparity Research.","authors":"Susan M. Paddock","doi":"10.1080/01621459.2024.2344715","DOIUrl":"https://doi.org/10.1080/01621459.2024.2344715","url":null,"abstract":"Published in Journal of the American Statistical Association (Just accepted, 2024)","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140608125","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
Selection and Aggregation of Conformal Prediction Sets 共形预测集的选择与聚合
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-04-17 DOI: 10.1080/01621459.2024.2344700
Yachong Yang, Arun Kumar Kuchibhotla
{"title":"Selection and Aggregation of Conformal Prediction Sets","authors":"Yachong Yang, Arun Kumar Kuchibhotla","doi":"10.1080/01621459.2024.2344700","DOIUrl":"https://doi.org/10.1080/01621459.2024.2344700","url":null,"abstract":"Conformal prediction is a generic methodology for finite-sample valid distribution-free prediction. This technique has garnered a lot of attention in the literature partly because it can be applied...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140608121","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
Introduction to Environmental Data Science 环境数据科学入门
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-04-16 DOI: 10.1080/01621459.2024.2343459
Timothée Poisot
{"title":"Introduction to Environmental Data Science","authors":"Timothée Poisot","doi":"10.1080/01621459.2024.2343459","DOIUrl":"https://doi.org/10.1080/01621459.2024.2343459","url":null,"abstract":"Published in Journal of the American Statistical Association (Just accepted, 2024)","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140603609","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
Dissecting gene expression heterogeneity: generalized Pearson correlation squares and the K-lines clustering algorithm 剖析基因表达异质性：广义皮尔逊相关平方和 K 线聚类算法
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-04-15 DOI: 10.1080/01621459.2024.2342639
Jingyi Jessica Li, Heather J. Zhou, Peter J. Bickel, Xin Tong
{"title":"Dissecting gene expression heterogeneity: generalized Pearson correlation squares and the K-lines clustering algorithm","authors":"Jingyi Jessica Li, Heather J. Zhou, Peter J. Bickel, Xin Tong","doi":"10.1080/01621459.2024.2342639","DOIUrl":"https://doi.org/10.1080/01621459.2024.2342639","url":null,"abstract":"Motivated by the pressing needs for dissecting heterogeneous relationships in gene expression data, here we generalize the squared Pearson correlation to capture a mixture of linear dependences bet...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140553391","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
Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning Sharp-SSL：用于半监督学习的选择性高维轴对齐随机投影
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-04-12 DOI: 10.1080/01621459.2024.2340792
Tengyao Wang, Edgar Dobriban, Milana Gataric, Richard J. Samworth
{"title":"Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning","authors":"Tengyao Wang, Edgar Dobriban, Milana Gataric, Richard J. Samworth","doi":"10.1080/01621459.2024.2340792","DOIUrl":"https://doi.org/10.1080/01621459.2024.2340792","url":null,"abstract":"We propose a new method for high-dimensional semi-supervised learning problems based on the careful aggregation of the results of a low-dimensional procedure applied to many axis-aligned random pro...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140547958","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
Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction 为联合气候重建中的不确定性量化建立可交换过程模型
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-04-11 DOI: 10.1080/01621459.2024.2325705
Lachlan Astfalck, Daniel Williamson, Niall Gandy, Lauren Gregoire, Ruza Ivanovic
{"title":"Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction","authors":"Lachlan Astfalck, Daniel Williamson, Niall Gandy, Lauren Gregoire, Ruza Ivanovic","doi":"10.1080/01621459.2024.2325705","DOIUrl":"https://doi.org/10.1080/01621459.2024.2325705","url":null,"abstract":"Any experiment with climate models relies on a potentially large set of spatio-temporal boundary conditions. These can represent both the initial state of the system and/or forcings driving the mod...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140544872","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
Sobolev Calibration of Imperfect Computer Models 不完善计算机模型的索波列夫校准
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-04-09 DOI: 10.1080/01621459.2024.2340793
Qingwen Zhang, Wenjia Wang
{"title":"Sobolev Calibration of Imperfect Computer Models","authors":"Qingwen Zhang, Wenjia Wang","doi":"10.1080/01621459.2024.2340793","DOIUrl":"https://doi.org/10.1080/01621459.2024.2340793","url":null,"abstract":"Calibration refers to the statistical estimation of unknown model parameters in computer experiments, such that computer experiments can match underlying physical systems. This work develops a new ...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140538775","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
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