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Improving tensor regression by optimal model averaging 通过优化模型平均改进张量回归
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-09-13 DOI: 10.1080/01621459.2024.2398164
Qiushi Bu, Hua Liang, Xinyu Zhang, Jiahui Zou
{"title":"Improving tensor regression by optimal model averaging","authors":"Qiushi Bu, Hua Liang, Xinyu Zhang, Jiahui Zou","doi":"10.1080/01621459.2024.2398164","DOIUrl":"https://doi.org/10.1080/01621459.2024.2398164","url":null,"abstract":"Tensors have broad applications in neuroimaging, data mining, digital marketing, etc. CANDECOMP/PARAFAC (CP) tensor decomposition can effectively reduce the number of parameters to gain dimensional...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317729","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
Off-policy Evaluation in Doubly Inhomogeneous Environments 双非均质环境中的非政策评估
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-09-09 DOI: 10.1080/01621459.2024.2395593
Zeyu Bian, Chengchun Shi, Zhengling Qi, Lan Wang
{"title":"Off-policy Evaluation in Doubly Inhomogeneous Environments","authors":"Zeyu Bian, Chengchun Shi, Zhengling Qi, Lan Wang","doi":"10.1080/01621459.2024.2395593","DOIUrl":"https://doi.org/10.1080/01621459.2024.2395593","url":null,"abstract":"This work aims to study off-policy evaluation (OPE) under scenarios where two key reinforcement learning (RL) assumptions – temporal stationarity and individual homogeneity are both violated. To ha...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170863","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
Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects 通过加权平均治疗效果评估治疗优先级规则
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-09-03 DOI: 10.1080/01621459.2024.2393466
Steve Yadlowsky, Scott Fleming, Nigam Shah, Emma Brunskill, Stefan Wager
{"title":"Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects","authors":"Steve Yadlowsky, Scott Fleming, Nigam Shah, Emma Brunskill, Stefan Wager","doi":"10.1080/01621459.2024.2393466","DOIUrl":"https://doi.org/10.1080/01621459.2024.2393466","url":null,"abstract":"There are a number of available methods for selecting whom to prioritize for treatment, including ones based on treatment effect estimation, risk scoring, and hand-crafted rules. We propose rank-we...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245503","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
Models for Multi-State Survival Data: Rates, Risks, and Pseudo-Values 多州生存数据模型：比率、风险和伪值
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-08-30 DOI: 10.1080/01621459.2024.2395590
Ross L. Prentice
{"title":"Models for Multi-State Survival Data: Rates, Risks, and Pseudo-Values","authors":"Ross L. Prentice","doi":"10.1080/01621459.2024.2395590","DOIUrl":"https://doi.org/10.1080/01621459.2024.2395590","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":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170462","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 Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison 无向高斯图形模型中的贝叶斯结构学习：文献综述与实证比较
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-08-30 DOI: 10.1080/01621459.2024.2395504
Lucas Vogels, Reza Mohammadi, Marit Schoonhoven, Ş. İlker Birbil
{"title":"Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison","authors":"Lucas Vogels, Reza Mohammadi, Marit Schoonhoven, Ş. İlker Birbil","doi":"10.1080/01621459.2024.2395504","DOIUrl":"https://doi.org/10.1080/01621459.2024.2395504","url":null,"abstract":"Gaussian graphical models provide a powerful framework to reveal the conditional dependency structure between multivariate variables. The process of uncovering the conditional dependency network is...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235238","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
Model-based causal feature selection for general response types 基于模型的一般反应类型因果特征选择
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-08-30 DOI: 10.1080/01621459.2024.2395588
Lucas Kook, Sorawit Saengkyongam, Anton Rask Lundborg, Torsten Hothorn, Jonas Peters
{"title":"Model-based causal feature selection for general response types","authors":"Lucas Kook, Sorawit Saengkyongam, Anton Rask Lundborg, Torsten Hothorn, Jonas Peters","doi":"10.1080/01621459.2024.2395588","DOIUrl":"https://doi.org/10.1080/01621459.2024.2395588","url":null,"abstract":"Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters et al., 2016) is a method for causal feature selection which...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142101092","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
Optimal Network Pairwise Comparison 最佳网络配对比较
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-08-28 DOI: 10.1080/01621459.2024.2393471
Jiashun Jin, Zheng Tracy Ke, Shengming Luo, Yucong Ma
{"title":"Optimal Network Pairwise Comparison","authors":"Jiashun Jin, Zheng Tracy Ke, Shengming Luo, Yucong Ma","doi":"10.1080/01621459.2024.2393471","DOIUrl":"https://doi.org/10.1080/01621459.2024.2393471","url":null,"abstract":"We are interested in the problem of two-sample network hypothesis testing: given two networks with the same set of nodes, we wish to test whether the underlying Bernoulli probability matrices of th...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142276070","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
Monte Carlo inference for semiparametric Bayesian regression 半参数贝叶斯回归的蒙特卡罗推论
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-08-28 DOI: 10.1080/01621459.2024.2395586
Daniel R. Kowal, Bohan Wu
{"title":"Monte Carlo inference for semiparametric Bayesian regression","authors":"Daniel R. Kowal, Bohan Wu","doi":"10.1080/01621459.2024.2395586","DOIUrl":"https://doi.org/10.1080/01621459.2024.2395586","url":null,"abstract":"Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically invo...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142234095","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
Semi-supervised Triply Robust Inductive Transfer Learning 半监督三重稳健归纳迁移学习
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-08-22 DOI: 10.1080/01621459.2024.2393463
Tianxi Cai, Mengyan Li, Molei Liu
{"title":"Semi-supervised Triply Robust Inductive Transfer Learning","authors":"Tianxi Cai, Mengyan Li, Molei Liu","doi":"10.1080/01621459.2024.2393463","DOIUrl":"https://doi.org/10.1080/01621459.2024.2393463","url":null,"abstract":"In this work, we propose a Semi-supervised Triply Robust Inductive transFer LEarning (STRIFLE) approach, which integrates heterogeneous data from a label-rich source population and a label-scarce t...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245504","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 Inference for Networks of High-Dimensional Point Processes 高维点过程网络的统计推断
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-08-22 DOI: 10.1080/01621459.2024.2392907
Xu Wang, Mladen Kolar, Ali Shojaie
{"title":"Statistical Inference for Networks of High-Dimensional Point Processes","authors":"Xu Wang, Mladen Kolar, Ali Shojaie","doi":"10.1080/01621459.2024.2392907","DOIUrl":"https://doi.org/10.1080/01621459.2024.2392907","url":null,"abstract":"Fueled in part by recent applications in neuroscience, the multivariate Hawkes process has become a popular tool for modeling the network of interactions among high-dimensional point process data. ...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142101053","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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