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AddiVortes: (Bayesian) Additive Voronoi Tessellations AddiVortes：（贝叶斯）加法沃罗诺网状结构
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-10-16 DOI: 10.1080/10618600.2024.2414104
Adam. J. Stone, John Paul Gosling
{"title":"AddiVortes: (Bayesian) Additive Voronoi Tessellations","authors":"Adam. J. Stone, John Paul Gosling","doi":"10.1080/10618600.2024.2414104","DOIUrl":"https://doi.org/10.1080/10618600.2024.2414104","url":null,"abstract":"The Additive Voronoi Tessellations (AddiVortes) model is a multivariate regression model that uses Voronoi tessellations to partition the covariate space in an additive ensemble model. Unlike other...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439805","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}
引用次数: 0
Sample efficient nonparametric regression via low-rank regularization 通过低秩正则化实现样本高效非参数回归
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-10-15 DOI: 10.1080/10618600.2024.2414891
Jiakun Jiang, Jiahao Peng, Heng Lian
{"title":"Sample efficient nonparametric regression via low-rank regularization","authors":"Jiakun Jiang, Jiahao Peng, Heng Lian","doi":"10.1080/10618600.2024.2414891","DOIUrl":"https://doi.org/10.1080/10618600.2024.2414891","url":null,"abstract":"Nonparametric regression suffers from curse of dimensionality, requiring a relatively large sample size for accurate estimation beyond the univariate case. In this paper, we consider a simple metho...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439935","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}
引用次数: 0
Scalable Clustering: Large Scale Unsupervised Learning of Gaussian Mixture Models with Outliers 可扩展聚类：有异常值的高斯混杂模型的大规模无监督学习
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-10-14 DOI: 10.1080/10618600.2024.2414889
Yijia Zhou, Kyle A. Gallivan, Adrian Barbu
{"title":"Scalable Clustering: Large Scale Unsupervised Learning of Gaussian Mixture Models with Outliers","authors":"Yijia Zhou, Kyle A. Gallivan, Adrian Barbu","doi":"10.1080/10618600.2024.2414889","DOIUrl":"https://doi.org/10.1080/10618600.2024.2414889","url":null,"abstract":"Clustering is a widely used technique with a long and rich history in a variety of areas. However, most existing algorithms do not scale well to large datasets, or are missing theoretical guarantee...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439807","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}
引用次数: 0
Efficient Modeling of Spatial Extremes over Large Geographical Domains 大地理区域空间极值的高效建模
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-10-08 DOI: 10.1080/10618600.2024.2409784
Arnab Hazra, Raphaël Huser, David Bolin
{"title":"Efficient Modeling of Spatial Extremes over Large Geographical Domains","authors":"Arnab Hazra, Raphaël Huser, David Bolin","doi":"10.1080/10618600.2024.2409784","DOIUrl":"https://doi.org/10.1080/10618600.2024.2409784","url":null,"abstract":"Various natural phenomena exhibit spatial extremal dependence at short spatial distances. However, existing models proposed in the spatial extremes literature often assume that extremal dependence ...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397716","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}
引用次数: 0
Differentially Private Inference for Compositional Data 组合数据的差异化私有推理
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-10-07 DOI: 10.1080/10618600.2024.2412174
Qi Guo, Andrés F. Barrientos, Víctor Peña
{"title":"Differentially Private Inference for Compositional Data","authors":"Qi Guo, Andrés F. Barrientos, Víctor Peña","doi":"10.1080/10618600.2024.2412174","DOIUrl":"https://doi.org/10.1080/10618600.2024.2412174","url":null,"abstract":"Confidential data, such as electronic health records, activity data from wearable devices, and geolocation data, are becoming increasingly prevalent. Differential privacy provides a framework to co...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444355","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}
引用次数: 0
MCMC for Bayesian nonparametric mixture modeling under differential privacy 差异隐私下贝叶斯非参数混合建模的 MCMC
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-10-07 DOI: 10.1080/10618600.2024.2410911
Mario Beraha, Stefano Favaro, Vinayak Rao
{"title":"MCMC for Bayesian nonparametric mixture modeling under differential privacy","authors":"Mario Beraha, Stefano Favaro, Vinayak Rao","doi":"10.1080/10618600.2024.2410911","DOIUrl":"https://doi.org/10.1080/10618600.2024.2410911","url":null,"abstract":"Estimating the probability density of a population while preserving the privacy of individuals in that population is an important and challenging problem that has received considerable attention in...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439968","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}
引用次数: 0
Network embedding-based directed community detection with unknown community number 基于网络嵌入的定向群落检测与未知群落编号
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-10-04 DOI: 10.1080/10618600.2024.2409789
Qingzhao Zhang, Jinlong Zhou, Mingyang Ren
{"title":"Network embedding-based directed community detection with unknown community number","authors":"Qingzhao Zhang, Jinlong Zhou, Mingyang Ren","doi":"10.1080/10618600.2024.2409789","DOIUrl":"https://doi.org/10.1080/10618600.2024.2409789","url":null,"abstract":"Community detection of network analysis plays an important role in numerous application areas, in which estimating the number of communities is a fundamental issue. However, many existing methods f...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486662","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}
引用次数: 0
Grid Point Approximation for Distributed Nonparametric Smoothing and Prediction 用于分布式非参数平滑和预测的网格点近似法
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-10-03 DOI: 10.1080/10618600.2024.2409817
Yuan Gao, Rui Pan, Feng Li, Riquan Zhang, Hansheng Wang
{"title":"Grid Point Approximation for Distributed Nonparametric Smoothing and Prediction","authors":"Yuan Gao, Rui Pan, Feng Li, Riquan Zhang, Hansheng Wang","doi":"10.1080/10618600.2024.2409817","DOIUrl":"https://doi.org/10.1080/10618600.2024.2409817","url":null,"abstract":"Kernel smoothing is a widely used nonparametric method in modern statistical analysis. The problem of efficiently conducting kernel smoothing for a massive dataset on a distributed system is a prob...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439969","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}
引用次数: 0
FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions FAStEN：高维函数回归中特征选择和估计的高效自适应方法
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-09-27 DOI: 10.1080/10618600.2024.2407464
Tobia Boschi, Lorenzo Testa, Francesca Chiaromonte, Matthew Reimherr
{"title":"FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions","authors":"Tobia Boschi, Lorenzo Testa, Francesca Chiaromonte, Matthew Reimherr","doi":"10.1080/10618600.2024.2407464","DOIUrl":"https://doi.org/10.1080/10618600.2024.2407464","url":null,"abstract":"Functional regression analysis is an established tool for many contemporary scientific applications. Regression problems involving large and complex data sets are ubiquitous, and feature selection ...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328966","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}
引用次数: 0
Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe) 协方差辅助多元惩罚加性回归（CoMPAdRe）
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-09-27 DOI: 10.1080/10618600.2024.2407453
Neel Desai, Veerabhadran Baladandayuthapani, Russell T. Shinohara, Jeffrey S. Morris
{"title":"Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe)","authors":"Neel Desai, Veerabhadran Baladandayuthapani, Russell T. Shinohara, Jeffrey S. Morris","doi":"10.1080/10618600.2024.2407453","DOIUrl":"https://doi.org/10.1080/10618600.2024.2407453","url":null,"abstract":"We propose a new method for the simultaneous selection and estimation of multivariate sparse additive models with correlated errors. Our method called Covariance Assisted Multivariate Penalized Add...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328965","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}
引用次数: 0
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