Pub Date : 2025-09-16DOI: 10.1080/10618600.2025.2560623
Chao Liu, Bin Du, Jiaqi Li, Junlong Zhao
{"title":"Influential observations detection by random projection in high-dimensional multivariate response linear model","authors":"Chao Liu, Bin Du, Jiaqi Li, Junlong Zhao","doi":"10.1080/10618600.2025.2560623","DOIUrl":"https://doi.org/10.1080/10618600.2025.2560623","url":null,"abstract":"","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"14 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-15DOI: 10.1080/10618600.2025.2560621
Yongkang Li, Joseph Mathews, Scott C. Schmidler
{"title":"On Gibbs Sampling for Endpoint-Conditioned Neighbor-Dependent Sequence Evolution Models","authors":"Yongkang Li, Joseph Mathews, Scott C. Schmidler","doi":"10.1080/10618600.2025.2560621","DOIUrl":"https://doi.org/10.1080/10618600.2025.2560621","url":null,"abstract":"","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"9 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-15DOI: 10.1080/10618600.2025.2560626
Li-Pang Chen, Jou-Chin Wu, Grace Y. Yi
{"title":"Semiparametric Estimation for Error-Prone Partially Linear Single-Index Models","authors":"Li-Pang Chen, Jou-Chin Wu, Grace Y. Yi","doi":"10.1080/10618600.2025.2560626","DOIUrl":"https://doi.org/10.1080/10618600.2025.2560626","url":null,"abstract":"","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"24 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1080/10618600.2025.2554671
Hu Sun, Yang Chen
{"title":"Conformalized Tensor Completion with Riemannian Optimization","authors":"Hu Sun, Yang Chen","doi":"10.1080/10618600.2025.2554671","DOIUrl":"https://doi.org/10.1080/10618600.2025.2554671","url":null,"abstract":"","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"22 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144995562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1080/10618600.2025.2554680
Clement Lee, Marco Battiston
{"title":"A Bayesian Nonparametric Stochastic Block Model for Directed Acyclic Graphs","authors":"Clement Lee, Marco Battiston","doi":"10.1080/10618600.2025.2554680","DOIUrl":"https://doi.org/10.1080/10618600.2025.2554680","url":null,"abstract":"","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"24 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144995773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-02DOI: 10.1080/10618600.2025.2554675
Luis Angel García-Escudero, Christian Hennig, Agustín Mayo-Iscar, Gianluca Morelli, Marco Riani
{"title":"Choice of trimming proportion and number of clusters in robust clustering based on trimming","authors":"Luis Angel García-Escudero, Christian Hennig, Agustín Mayo-Iscar, Gianluca Morelli, Marco Riani","doi":"10.1080/10618600.2025.2554675","DOIUrl":"https://doi.org/10.1080/10618600.2025.2554675","url":null,"abstract":"","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"302 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144930898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-02-10DOI: 10.1080/10618600.2024.2431057
Yuanxing Chen, Qingzhao Zhang, Shuangge Ma
In functional data analysis, unsupervised clustering has been extensively conducted and has important implications. In most of the existing functional clustering analyses, it is assumed that there is a single clustering structure across the whole domain of measurement (say, time interval). In some data analyses, for example, the analysis of normalized COVID-19 daily confirmed cases for the U.S. states, it is observed that functions can have different clustering patterns in different time subintervals. To tackle the lack of flexibility of the existing functional clustering techniques, we develop a local clustering approach, which can fully data-dependently identify subintervals, where, in different subintervals, functions have different clustering structures. This approach is built on the basis expansion technique and has a novel penalization form. It simultaneously achieves subinterval identification, clustering, and estimation. Its estimation and clustering consistency properties are rigorously established. In simulation, it significantly outperforms multiple competitors. In the analysis of the COVID-19 case trajectory data, it identifies sensible subintervals and clustering structures. Supplementary materials for this article are available online.
{"title":"Local Clustering for Functional Data.","authors":"Yuanxing Chen, Qingzhao Zhang, Shuangge Ma","doi":"10.1080/10618600.2024.2431057","DOIUrl":"10.1080/10618600.2024.2431057","url":null,"abstract":"<p><p>In functional data analysis, unsupervised clustering has been extensively conducted and has important implications. In most of the existing functional clustering analyses, it is assumed that there is a single clustering structure across the whole domain of measurement (say, time interval). In some data analyses, for example, the analysis of normalized COVID-19 daily confirmed cases for the U.S. states, it is observed that functions can have different clustering patterns in different time subintervals. To tackle the lack of flexibility of the existing functional clustering techniques, we develop a local clustering approach, which can fully data-dependently identify subintervals, where, in different subintervals, functions have different clustering structures. This approach is built on the basis expansion technique and has a novel penalization form. It simultaneously achieves subinterval identification, clustering, and estimation. Its estimation and clustering consistency properties are rigorously established. In simulation, it significantly outperforms multiple competitors. In the analysis of the COVID-19 case trajectory data, it identifies sensible subintervals and clustering structures. Supplementary materials for this article are available online.</p>","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"34 3","pages":"1075-1090"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12588091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145458747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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