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Two sample test for covariance matrices in ultra-high dimension 超高维协方差矩阵的两次抽样检验
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-11-04 DOI: 10.1080/01621459.2024.2423971
Xiucai Ding, Yichen Hu, Zhenggang Wang
{"title":"Two sample test for covariance matrices in ultra-high dimension","authors":"Xiucai Ding, Yichen Hu, Zhenggang Wang","doi":"10.1080/01621459.2024.2423971","DOIUrl":"https://doi.org/10.1080/01621459.2024.2423971","url":null,"abstract":"In this paper, we propose a new test for testing the equality of two population covariance matrices in the ultra-high dimensional setting that the dimension is much larger than the sizes of both of...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"16 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142589055","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
Bayesian Nonparametrics for Causal Inference and Missing Data 用于因果推断和缺失数据的贝叶斯非参数法
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-11-04 DOI: 10.1080/01621459.2024.2423435
P. Richard Hahn
{"title":"Bayesian Nonparametrics for Causal Inference and Missing Data","authors":"P. Richard Hahn","doi":"10.1080/01621459.2024.2423435","DOIUrl":"https://doi.org/10.1080/01621459.2024.2423435","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":"59 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142589056","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 and computational efficiency for smooth tensor estimation with unknown permutations 未知排列的平滑张量估算的统计和计算效率
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-10-25 DOI: 10.1080/01621459.2024.2419114
Chanwoo Lee, Miaoyan Wang
{"title":"Statistical and computational efficiency for smooth tensor estimation with unknown permutations","authors":"Chanwoo Lee, Miaoyan Wang","doi":"10.1080/01621459.2024.2419114","DOIUrl":"https://doi.org/10.1080/01621459.2024.2419114","url":null,"abstract":"We consider the problem of structured tensor denoising in the presence of unknown permutations. Such data problems arise commonly in recommendation systems, neuroimaging, community detection, and m...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"3 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142556221","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
Matrix GARCH model: Inference and application* 矩阵 GARCH 模型：推理与应用*
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-10-18 DOI: 10.1080/01621459.2024.2415719
Cheng Yu, Dong Li, Feiyu Jiang, Ke Zhu
{"title":"Matrix GARCH model: Inference and application*","authors":"Cheng Yu, Dong Li, Feiyu Jiang, Ke Zhu","doi":"10.1080/01621459.2024.2415719","DOIUrl":"https://doi.org/10.1080/01621459.2024.2415719","url":null,"abstract":"Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroskedasticity that is often observed in economic and financi...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"3 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486731","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
eDNAPlus: A unifying modelling framework for DNA-based biodiversity monitoring eDNAPlus：基于 DNA 的生物多样性监测统一建模框架
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-10-18 DOI: 10.1080/01621459.2024.2412362
Alex Diana, Eleni Matechou, Jim Griffin, Douglas W. Yu, Mingjie Luo, Marie Tosa, Alex Bush, Richard Griffiths
{"title":"eDNAPlus: A unifying modelling framework for DNA-based biodiversity monitoring","authors":"Alex Diana, Eleni Matechou, Jim Griffin, Douglas W. Yu, Mingjie Luo, Marie Tosa, Alex Bush, Richard Griffiths","doi":"10.1080/01621459.2024.2412362","DOIUrl":"https://doi.org/10.1080/01621459.2024.2412362","url":null,"abstract":"DNA-based biodiversity surveys, which involve collecting physical samples from survey sites and assaying them in the laboratory to detect species via their diagnostic DNA sequences, are increasingl...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"79 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142556222","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
On the Modelling and Prediction of High-Dimensional Functional Time Series 论高维函数时间序列的建模与预测
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-10-15 DOI: 10.1080/01621459.2024.2413201
Jinyuan Chang, Qin Fang, Xinghao Qiao, Qiwei Yao
{"title":"On the Modelling and Prediction of High-Dimensional Functional Time Series","authors":"Jinyuan Chang, Qin Fang, Xinghao Qiao, Qiwei Yao","doi":"10.1080/01621459.2024.2413201","DOIUrl":"https://doi.org/10.1080/01621459.2024.2413201","url":null,"abstract":"We propose a two-step procedure to model and predict high-dimensional functional time series, where the number of function-valued time series p is large in relation to the length of time series n. ...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"8 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142490564","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
Deep regression learning with optimal loss function 具有最佳损失函数的深度回归学习
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-10-15 DOI: 10.1080/01621459.2024.2412364
Xuancheng Wang, Ling Zhou, Huazhen Lin
{"title":"Deep regression learning with optimal loss function","authors":"Xuancheng Wang, Ling Zhou, Huazhen Lin","doi":"10.1080/01621459.2024.2412364","DOIUrl":"https://doi.org/10.1080/01621459.2024.2412364","url":null,"abstract":"In this paper, we develop a novel efficient and robust nonparametric regression estimator under a framework of a feedforward neural network (FNN). There are several interesting characteristics for ...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"32 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142589062","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
Robust Permutation Tests in Linear Instrumental Variables Regression 线性工具变量回归中的稳健置换检验
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-10-15 DOI: 10.1080/01621459.2024.2412363
Purevdorj Tuvaandorj
{"title":"Robust Permutation Tests in Linear Instrumental Variables Regression","authors":"Purevdorj Tuvaandorj","doi":"10.1080/01621459.2024.2412363","DOIUrl":"https://doi.org/10.1080/01621459.2024.2412363","url":null,"abstract":"This paper develops permutation versions of identification-robust tests in linear instrumental variables regression. Unlike the existing randomization and rank-based tests in which independence bet...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"106 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448730","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
Additive Covariance Matrix Models: Modelling Regional Electricity Net-Demand in Great Britain Additive Covariance Matrix Models：英国地区电力净需求建模
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-10-11 DOI: 10.1080/01621459.2024.2412361
Vincenzo Gioia, Matteo Fasiolo, Jethro Browell, Ruggero Bellio
{"title":"Additive Covariance Matrix Models: Modelling Regional Electricity Net-Demand in Great Britain","authors":"Vincenzo Gioia, Matteo Fasiolo, Jethro Browell, Ruggero Bellio","doi":"10.1080/01621459.2024.2412361","DOIUrl":"https://doi.org/10.1080/01621459.2024.2412361","url":null,"abstract":"Forecasts of regional electricity net-demand, consumption minus embedded generation, are an essential input for reliable and economic power system operation, and energy trading. While such forecast...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"70 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448729","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
Operationalizing Legislative Bodies: A Methodological and Empirical Perspective with a Bayesian Approach 立法机构的可操作性：贝叶斯方法的方法论和实证视角
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-10-10 DOI: 10.1080/01621459.2024.2413928
Carolina Luque, Juan Sosa
{"title":"Operationalizing Legislative Bodies: A Methodological and Empirical Perspective with a Bayesian Approach","authors":"Carolina Luque, Juan Sosa","doi":"10.1080/01621459.2024.2413928","DOIUrl":"https://doi.org/10.1080/01621459.2024.2413928","url":null,"abstract":"This manuscript extensively reviews applications, extensions, and models derived from the Bayesian ideal point estimator. We focus our attention on studies conducted in the United States as well as...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"58 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486732","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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