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Using Penalized Synthetic Controls on Truncated data: A Case Study on Effect of Marijuana Legalization on Direct Payments to Physicians by Opioid Manufacturers 对截断数据使用惩罚性合成控制：大麻合法化对阿片类药物制造商向医生直接付款的影响案例研究
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-09-26 DOI: 10.1080/01621459.2024.2406583
Bikram Karmakar, Gourab Mukherjee, Wreetabrata Kar
{"title":"Using Penalized Synthetic Controls on Truncated data: A Case Study on Effect of Marijuana Legalization on Direct Payments to Physicians by Opioid Manufacturers","authors":"Bikram Karmakar, Gourab Mukherjee, Wreetabrata Kar","doi":"10.1080/01621459.2024.2406583","DOIUrl":"https://doi.org/10.1080/01621459.2024.2406583","url":null,"abstract":"Amid increasing awareness regarding opioid addiction, medical marijuana has emerged as a substitute to opioids for pain management. Concurrently, opioid manufacturers are putting significant resear...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142325562","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 Higher-Order Mixed Memberships via the ℓ2,∞ Tensor Perturbation Bound 通过 ℓ2,∞ 张量扰动约束估算高阶混合成员资格
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-09-23 DOI: 10.1080/01621459.2024.2404265
Joshua Agterberg, Anru R. Zhang
{"title":"Estimating Higher-Order Mixed Memberships via the ℓ2,∞ Tensor Perturbation Bound","authors":"Joshua Agterberg, Anru R. Zhang","doi":"10.1080/01621459.2024.2404265","DOIUrl":"https://doi.org/10.1080/01621459.2024.2404265","url":null,"abstract":"Higher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membe...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317731","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 estimation for number of factors in high dimensional factor modeling via Spearman correlation matrix 通过斯皮尔曼相关矩阵对高维因子模型中的因子数量进行稳健估计
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-09-20 DOI: 10.1080/01621459.2024.2402565
Jiaxin Qiu, Zeng Li, Jianfeng Yao
{"title":"Robust estimation for number of factors in high dimensional factor modeling via Spearman correlation matrix","authors":"Jiaxin Qiu, Zeng Li, Jianfeng Yao","doi":"10.1080/01621459.2024.2402565","DOIUrl":"https://doi.org/10.1080/01621459.2024.2402565","url":null,"abstract":"Determining the number of factors in high-dimensional factor modeling is essential but challenging, especially when the data are heavy-tailed. In this paper, we introduce a new estimator based on t...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142276071","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
Neyman-Pearson Multi-class Classification via Cost-sensitive Learning 通过成本敏感型学习进行奈曼-皮尔逊多类分类
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-09-20 DOI: 10.1080/01621459.2024.2402567
Ye Tian, Yang Feng
{"title":"Neyman-Pearson Multi-class Classification via Cost-sensitive Learning","authors":"Ye Tian, Yang Feng","doi":"10.1080/01621459.2024.2402567","DOIUrl":"https://doi.org/10.1080/01621459.2024.2402567","url":null,"abstract":"Most existing classification methods aim to minimize the overall misclassification error rate. However, in applications such as loan default prediction, different types of errors can have varying c...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317732","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
An Adaptive Transfer Learning Framework for Functional Classification 功能分类的自适应迁移学习框架
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-09-20 DOI: 10.1080/01621459.2024.2403788
Caihong Qin, Jinhan Xie, Ting Li, Yang Bai
{"title":"An Adaptive Transfer Learning Framework for Functional Classification","authors":"Caihong Qin, Jinhan Xie, Ting Li, Yang Bai","doi":"10.1080/01621459.2024.2403788","DOIUrl":"https://doi.org/10.1080/01621459.2024.2403788","url":null,"abstract":"In this paper, we study the transfer learning problem in functional classification, aiming to improve the classification accuracy of the target data by leveraging information from related source da...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374037","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
Large-Scale Low-Rank Gaussian Process Prediction with Support Points 带支持点的大规模低库高斯过程预测
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-09-20 DOI: 10.1080/01621459.2024.2403188
Yan Song, Wenlin Dai, Marc G. Genton
{"title":"Large-Scale Low-Rank Gaussian Process Prediction with Support Points","authors":"Yan Song, Wenlin Dai, Marc G. Genton","doi":"10.1080/01621459.2024.2403188","DOIUrl":"https://doi.org/10.1080/01621459.2024.2403188","url":null,"abstract":"Low-rank approximation is a popular strategy to tackle the “big n problem” associated with large-scale Gaussian process regressions. Basis functions for developing low-rank structures are crucial a...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142276069","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
A Model-Agnostic Graph Neural Network for Integrating Local and Global Information 用于整合本地和全球信息的模型诊断图神经网络
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-09-20 DOI: 10.1080/01621459.2024.2404668
Wenzhuo Zhou, Annie Qu, Keiland W. Cooper, Norbert Fortin, Babak Shahbaba
{"title":"A Model-Agnostic Graph Neural Network for Integrating Local and Global Information","authors":"Wenzhuo Zhou, Annie Qu, Keiland W. Cooper, Norbert Fortin, Babak Shahbaba","doi":"10.1080/01621459.2024.2404668","DOIUrl":"https://doi.org/10.1080/01621459.2024.2404668","url":null,"abstract":"Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limitations: a lack o...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142384072","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
Bisection Grover’s Search Algorithm and Its Application in Analyzing CITE-seq Data 分段格罗弗搜索算法及其在分析 CITE-seq 数据中的应用
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-09-20 DOI: 10.1080/01621459.2024.2404259
Ping Ma, Yongkai Chen, Haoran Lu, Wenxuan Zhong
{"title":"Bisection Grover’s Search Algorithm and Its Application in Analyzing CITE-seq Data","authors":"Ping Ma, Yongkai Chen, Haoran Lu, Wenxuan Zhong","doi":"10.1080/01621459.2024.2404259","DOIUrl":"https://doi.org/10.1080/01621459.2024.2404259","url":null,"abstract":"With the rapid development of quantum computers, researchers have shown quantum advantages in physics-oriented problems. Quantum algorithms tackling computational biology problems are still lacking...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142276068","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 clustering of categorical data based on the Hamming distance 基于汉明距离的分类数据模型聚类
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-09-20 DOI: 10.1080/01621459.2024.2402568
Raffaele Argiento, Edoardo Filippi-Mazzola, Lucia Paci
{"title":"Model-based clustering of categorical data based on the Hamming distance","authors":"Raffaele Argiento, Edoardo Filippi-Mazzola, Lucia Paci","doi":"10.1080/01621459.2024.2402568","DOIUrl":"https://doi.org/10.1080/01621459.2024.2402568","url":null,"abstract":"A model-based approach is developed for clustering categorical data with no natural ordering. The proposed method exploits the Hamming distance to define a family of probability mass functions to m...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142325564","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 Optimality of Mallows Model Averaging*† 论马洛模型平均化的最优性*†
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-09-20 DOI: 10.1080/01621459.2024.2402566
Jingfu Peng, Yang Li, Yuhong Yang
{"title":"On Optimality of Mallows Model Averaging*†","authors":"Jingfu Peng, Yang Li, Yuhong Yang","doi":"10.1080/01621459.2024.2402566","DOIUrl":"https://doi.org/10.1080/01621459.2024.2402566","url":null,"abstract":"In the past decades, model averaging (MA) has attracted much attention as it has emerged as an alternative tool to the model selection (MS) statistical approach. Hansen (2007) introduced a Mallows ...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142325359","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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