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}
Pub Date : 2025-08-26DOI: 10.1080/10618600.2025.2551270
Wenlong Jiang, Chris McKennan, Jesús Arroyo, Joshua Cape
{"title":"Simultaneous estimation of connectivity and dimensionality in samples of networks","authors":"Wenlong Jiang, Chris McKennan, Jesús Arroyo, Joshua Cape","doi":"10.1080/10618600.2025.2551270","DOIUrl":"https://doi.org/10.1080/10618600.2025.2551270","url":null,"abstract":"","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"55 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144906064","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 : 2025-08-26DOI: 10.1080/10618600.2025.2551271
Beniamino Hadj-Amar, Aaron M. Bornstein, Michele Guindani, Marina Vannucci
{"title":"Discrete Autoregressive Switching Processes with Cumulative Shrinkage Priors for Graphical Modeling of Time Series Data","authors":"Beniamino Hadj-Amar, Aaron M. Bornstein, Michele Guindani, Marina Vannucci","doi":"10.1080/10618600.2025.2551271","DOIUrl":"https://doi.org/10.1080/10618600.2025.2551271","url":null,"abstract":"","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"23 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144906065","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 : 2025-08-25DOI: 10.1080/10618600.2025.2549110
Siddhartha Nandy, Minwoo Kim, Shrijita Bhattacharya, Tapabrata Maiti
{"title":"Variational Inference Aided Variable Selection For Spatially Structured High Dimensional Covariates","authors":"Siddhartha Nandy, Minwoo Kim, Shrijita Bhattacharya, Tapabrata Maiti","doi":"10.1080/10618600.2025.2549110","DOIUrl":"https://doi.org/10.1080/10618600.2025.2549110","url":null,"abstract":"","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"193 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900107","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 : 2025-08-22DOI: 10.1080/10618600.2025.2546446
Meïli Baragatti, Céline Casenave, Bertrand Cloez, David Métivier, Isabelle Sanchez
{"title":"Approximate Bayesian Computation with Deep Learning and Conformal prediction","authors":"Meïli Baragatti, Céline Casenave, Bertrand Cloez, David Métivier, Isabelle Sanchez","doi":"10.1080/10618600.2025.2546446","DOIUrl":"https://doi.org/10.1080/10618600.2025.2546446","url":null,"abstract":"","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"16 1","pages":"1-35"},"PeriodicalIF":2.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900109","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}
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