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Ultra-efficient MCMC for Bayesian longitudinal functional data analysis 用于贝叶斯纵向功能数据分析的超高效 MCMC
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-06-07 DOI: 10.1080/10618600.2024.2362227
Thomas Y. Sun, Daniel R. Kowal
{"title":"Ultra-efficient MCMC for Bayesian longitudinal functional data analysis","authors":"Thomas Y. Sun, Daniel R. Kowal","doi":"10.1080/10618600.2024.2362227","DOIUrl":"https://doi.org/10.1080/10618600.2024.2362227","url":null,"abstract":"Functional mixed models are widely useful for regression analysis with dependent functional data, including longitudinal functional data with scalar predictors. However, existing algorithms for Bay...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"36 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141309175","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
Multivariate Singular Spectrum Analysis by Robust Diagonalwise Low-Rank Approximation 通过鲁棒对角线低方根逼近进行多变量奇异频谱分析
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-06-05 DOI: 10.1080/10618600.2024.2362222
Fabio Centofanti, Mia Hubert, Biagio Palumbo, Peter J. Rousseeuw
{"title":"Multivariate Singular Spectrum Analysis by Robust Diagonalwise Low-Rank Approximation","authors":"Fabio Centofanti, Mia Hubert, Biagio Palumbo, Peter J. Rousseeuw","doi":"10.1080/10618600.2024.2362222","DOIUrl":"https://doi.org/10.1080/10618600.2024.2362222","url":null,"abstract":"Multivariate Singular Spectrum Analysis (MSSA) is a powerful and widely used nonparametric method for multivariate time series, which allows the analysis of complex temporal data from diverse field...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"175 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425524","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
Class-Distributed Learning for Multinomial Logistic Regression with High Dimensional Features and a Large Number of Classes 具有高维特征和大量类别的多项式逻辑回归的类别分布式学习
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-05-31 DOI: 10.1080/10618600.2024.2362230
Shuyuan Wu, Jing Zhou, Ke Xu, Hansheng Wang
{"title":"Class-Distributed Learning for Multinomial Logistic Regression with High Dimensional Features and a Large Number of Classes","authors":"Shuyuan Wu, Jing Zhou, Ke Xu, Hansheng Wang","doi":"10.1080/10618600.2024.2362230","DOIUrl":"https://doi.org/10.1080/10618600.2024.2362230","url":null,"abstract":"Estimating a high-dimensional multinomial logistic regression model with a larger number of categories is of fundamental importance but it presents two challenges. Computationally, it leads to heav...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"24 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141308962","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
Versatile Descent Algorithms for Group Regularization and Variable Selection in Generalized Linear Models 通用线性模型中分组正规化和变量选择的多功能后裔算法
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-05-31 DOI: 10.1080/10618600.2024.2362232
Nathaniel E. Helwig
{"title":"Versatile Descent Algorithms for Group Regularization and Variable Selection in Generalized Linear Models","authors":"Nathaniel E. Helwig","doi":"10.1080/10618600.2024.2362232","DOIUrl":"https://doi.org/10.1080/10618600.2024.2362232","url":null,"abstract":"This paper proposes an adaptively bounded gradient descent (ABGD) algorithm for group elastic net penalized regression. Unlike previously proposed algorithms, the proposed algorithm adaptively boun...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"33 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141309048","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
Iterated Data Sharpening 迭代数据锐化
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-05-31 DOI: 10.1080/10618600.2024.2362219
Hanxiao Chen, W. John Braun, Xiaoping Shi
{"title":"Iterated Data Sharpening","authors":"Hanxiao Chen, W. John Braun, Xiaoping Shi","doi":"10.1080/10618600.2024.2362219","DOIUrl":"https://doi.org/10.1080/10618600.2024.2362219","url":null,"abstract":"Data sharpening in kernel regression has been shown to be an effective method of reducing bias while having minimal effects on variance. Earlier efforts to iterate the data sharpening procedure hav...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"315 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141309191","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
Multiple-use calibration for all future values and exact two-sided simultaneous tolerance intervals in linear regression 线性回归中所有未来值和精确双侧同时容差区间的多用途校准
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-05-28 DOI: 10.1080/10618600.2024.2359507
Yang Han, Lingjiao Wang, Wei Liu, Frank Bretz
{"title":"Multiple-use calibration for all future values and exact two-sided simultaneous tolerance intervals in linear regression","authors":"Yang Han, Lingjiao Wang, Wei Liu, Frank Bretz","doi":"10.1080/10618600.2024.2359507","DOIUrl":"https://doi.org/10.1080/10618600.2024.2359507","url":null,"abstract":"Multiple-use calibration using regression is an important statistical tool. Confidence sets for the x-values associated with all future y-values should guarantee a key property, which can be satisf...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"60 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315684","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
Dynamic Survival Prediction Using Sparse Longitudinal Images via Multi-Dimensional Functional Principal Component Analysis 通过多维功能主成分分析利用稀疏纵向图像进行动态生存预测
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-05-23 DOI: 10.1080/10618600.2024.2335182
Haolun Shi, Shu Jiang, Da Ma, Mirza Faisal Beg, Jiguo Cao
{"title":"Dynamic Survival Prediction Using Sparse Longitudinal Images via Multi-Dimensional Functional Principal Component Analysis","authors":"Haolun Shi, Shu Jiang, Da Ma, Mirza Faisal Beg, Jiguo Cao","doi":"10.1080/10618600.2024.2335182","DOIUrl":"https://doi.org/10.1080/10618600.2024.2335182","url":null,"abstract":"Our work is motivated by predicting the progression of Alzheimer’s disease (AD) based on a series of longitudinally observed brain scan images. Existing works on dynamic prediction for AD focus pri...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"137 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091897","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
smashGP: Large-scale Spatial Modeling via Matrix-free Gaussian Processes smashGP：通过无矩阵高斯过程进行大规模空间建模
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-05-22 DOI: 10.1080/10618600.2024.2353653
Lucas Erlandson, Ana María Estrada Gómez, Edmond Chow, Kamran Paynabar
{"title":"smashGP: Large-scale Spatial Modeling via Matrix-free Gaussian Processes","authors":"Lucas Erlandson, Ana María Estrada Gómez, Edmond Chow, Kamran Paynabar","doi":"10.1080/10618600.2024.2353653","DOIUrl":"https://doi.org/10.1080/10618600.2024.2353653","url":null,"abstract":"Gaussian processes are essential for spatial data analysis. Not only do they allow the prediction of unknown values, but they also allow for uncertainty quantification. However, in the era of big d...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"43 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091856","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
Nonparametric high-dimensional multi-sample tests based on graph theory 基于图论的非参数高维多样本检验
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-05-21 DOI: 10.1080/10618600.2024.2358156
Xiaoping Shi
{"title":"Nonparametric high-dimensional multi-sample tests based on graph theory","authors":"Xiaoping Shi","doi":"10.1080/10618600.2024.2358156","DOIUrl":"https://doi.org/10.1080/10618600.2024.2358156","url":null,"abstract":"High-dimensional data pose unique challenges for data processing in an era of ever-increasing amounts of data availability. Graph theory can provide a structure of high-dimensional data. We introdu...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"5 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091862","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
Nonparametric testing of the covariate significance for spatial point patterns under the presence of nuisance covariates 在存在干扰协变量的情况下，对空间点模式的协变量显著性进行非参数检验
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-05-20 DOI: 10.1080/10618600.2024.2357626
Jiří Dvořák, Tomáš Mrkvička
{"title":"Nonparametric testing of the covariate significance for spatial point patterns under the presence of nuisance covariates","authors":"Jiří Dvořák, Tomáš Mrkvička","doi":"10.1080/10618600.2024.2357626","DOIUrl":"https://doi.org/10.1080/10618600.2024.2357626","url":null,"abstract":"Determining the relevant spatial covariates is one of the most important problems in the analysis of point patterns. Parametric methods may lead to incorrect conclusions, especially when the model ...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":"53 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091860","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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