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Robust regression with covariate filtering: Heavy tails and adversarial contamination 带有协变量过滤的稳健回归：重尾和对抗性污染
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-08-22 DOI: 10.1080/01621459.2024.2392906
Ankit Pensia, Varun Jog, Po-Ling Loh
{"title":"Robust regression with covariate filtering: Heavy tails and adversarial contamination","authors":"Ankit Pensia, Varun Jog, Po-Ling Loh","doi":"10.1080/01621459.2024.2392906","DOIUrl":"https://doi.org/10.1080/01621459.2024.2392906","url":null,"abstract":"We study the problem of linear regression where both covariates and responses are potentially (i) heavy-tailed and (ii) adversarially contaminated. Several computationally efficient estimators have...","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":"142276072","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
Natural Gradient Variational Bayes without Fisher Matrix Analytic Calculation and Its Inversion 无费雪矩阵分析计算的自然梯度变异贝叶斯及其反演
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-08-22 DOI: 10.1080/01621459.2024.2392904
A. Godichon-Baggioni, D. Nguyen, M-N. Tran
{"title":"Natural Gradient Variational Bayes without Fisher Matrix Analytic Calculation and Its Inversion","authors":"A. Godichon-Baggioni, D. Nguyen, M-N. Tran","doi":"10.1080/01621459.2024.2392904","DOIUrl":"https://doi.org/10.1080/01621459.2024.2392904","url":null,"abstract":"This paper introduces a method for efficiently approximating the inverse of the Fisher information matrix, a crucial step in achieving effective variational Bayes inference. A notable aspect of our...","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":"142235080","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
Efficient Multiple Change Point Detection and Localization For High-dimensional Quantile Regression with Heteroscedasticity 具有异方差的高维量子回归的高效多变化点检测和定位
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-08-19 DOI: 10.1080/01621459.2024.2392903
Xianru Wang, Bin Liu, Xinsheng Zhang, Yufeng Liu
{"title":"Efficient Multiple Change Point Detection and Localization For High-dimensional Quantile Regression with Heteroscedasticity","authors":"Xianru Wang, Bin Liu, Xinsheng Zhang, Yufeng Liu","doi":"10.1080/01621459.2024.2392903","DOIUrl":"https://doi.org/10.1080/01621459.2024.2392903","url":null,"abstract":"Data heterogeneity is a challenging issue for modern statistical data analysis. There are different types of data heterogeneity in practice. In this paper, we consider potential structural changes ...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245505","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
Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data 高维多平台临床基因组数据的功能整合贝叶斯分析
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-08-14 DOI: 10.1080/01621459.2024.2388909
Rupam Bhattacharyya, Nicholas C. Henderson, Veerabhadran Baladandayuthapani
{"title":"Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data","authors":"Rupam Bhattacharyya, Nicholas C. Henderson, Veerabhadran Baladandayuthapani","doi":"10.1080/01621459.2024.2388909","DOIUrl":"https://doi.org/10.1080/01621459.2024.2388909","url":null,"abstract":"Rapid advancements in collection and dissemination of multi-platform molecular and genomics data has resulted in enormous opportunities to aggregate such data in order to understand, prevent, and t...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998732","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
Parallel sampling of decomposable graphs using Markov chains on junction trees 使用结点树上的马尔可夫链对可分解图进行并行采样
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-08-14 DOI: 10.1080/01621459.2024.2388908
Mohamad Elmasri
{"title":"Parallel sampling of decomposable graphs using Markov chains on junction trees","authors":"Mohamad Elmasri","doi":"10.1080/01621459.2024.2388908","DOIUrl":"https://doi.org/10.1080/01621459.2024.2388908","url":null,"abstract":"Bayesian inference for undirected graphical models is mostly restricted to the class of decomposable graphs, as they enjoy a rich set of properties making them amenable to high-dimensional problems...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998698","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 Membership Estimation under Severe Degree Heterogeneity 严重程度异质性下的最优网络成员估计
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-08-13 DOI: 10.1080/01621459.2024.2388903
Zheng Tracy Ke, Jingming Wang
{"title":"Optimal Network Membership Estimation under Severe Degree Heterogeneity","authors":"Zheng Tracy Ke, Jingming Wang","doi":"10.1080/01621459.2024.2388903","DOIUrl":"https://doi.org/10.1080/01621459.2024.2388903","url":null,"abstract":"Real networks often have severe degree heterogeneity, with maximum, average, and minimum node degrees differing significantly. This paper examines the impact of degree heterogeneity on statistical ...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986515","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 of Cell-type Proportions Estimated from Bulk Expression Data 从大量表达数据估算细胞类型比例的统计推断
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-07-23 DOI: 10.1080/01621459.2024.2382435
Biao Cai, Emma Jingfei Zhang, Hongyu Li, Chang Su, Hongyu Zhao
{"title":"Statistical Inference of Cell-type Proportions Estimated from Bulk Expression Data","authors":"Biao Cai, Emma Jingfei Zhang, Hongyu Li, Chang Su, Hongyu Zhao","doi":"10.1080/01621459.2024.2382435","DOIUrl":"https://doi.org/10.1080/01621459.2024.2382435","url":null,"abstract":"There is a growing interest in cell-type-specific analysis from bulk samples with a mixture of different cell types. A critical first step in such analyses is the accurate estimation of cell-type p...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141764411","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 Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models* 非随机缺失时的矩阵补全及其在因果面板数据模型中的应用*
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-07-17 DOI: 10.1080/01621459.2024.2380105
Jungjun Choi, Ming Yuan
{"title":"Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models*","authors":"Jungjun Choi, Ming Yuan","doi":"10.1080/01621459.2024.2380105","DOIUrl":"https://doi.org/10.1080/01621459.2024.2380105","url":null,"abstract":"This paper develops an inferential framework for matrix completion when missing is not at random and without the requirement of strong signals. Our development is based on the observation that if t...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730625","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
Controlling the False Split Rate in Tree-Based Aggregation 控制树状聚合中的误分率
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-07-09 DOI: 10.1080/01621459.2024.2376285
Simeng Shao, Jacob Bien, Adel Javanmard
{"title":"Controlling the False Split Rate in Tree-Based Aggregation","authors":"Simeng Shao, Jacob Bien, Adel Javanmard","doi":"10.1080/01621459.2024.2376285","DOIUrl":"https://doi.org/10.1080/01621459.2024.2376285","url":null,"abstract":"In many domains, data measurements can naturally be associated with the leaves of a tree, expressing the relationships among these measurements. For example, companies belong to industries, which i...","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602711","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
Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests 高维数据的稀疏图形建模：条件独立性检验范例
IF 3.7 1区 数学
Journal of the American Statistical Association Pub Date : 2024-07-08 DOI: 10.1080/01621459.2024.2375035
Reza Mohammadi
{"title":"Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests","authors":"Reza Mohammadi","doi":"10.1080/01621459.2024.2375035","DOIUrl":"https://doi.org/10.1080/01621459.2024.2375035","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":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597507","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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