发布求助
文献互助 智能选刊 最新文献

Journal of Computational and Graphical Statistics最新文献

筛选
英文 中文
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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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
A Projection Approach to Local Regression with Variable-Dimension Covariates 使用变维度变量进行局部回归的投影方法
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-05-20 DOI: 10.1080/10618600.2024.2357636
Matthew J. Heiner, Garritt L. Page, Fernando Andrés Quintana
{"title":"A Projection Approach to Local Regression with Variable-Dimension Covariates","authors":"Matthew J. Heiner, Garritt L. Page, Fernando Andrés Quintana","doi":"10.1080/10618600.2024.2357636","DOIUrl":"https://doi.org/10.1080/10618600.2024.2357636","url":null,"abstract":"Incomplete covariate vectors are known to be problematic for estimation and inferences on model parameters, but their impact on prediction performance is less understood. We develop an imputation-f...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091905","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
Fast calculation of Gaussian process multiple-fold cross-validation residuals and their covariances 高斯过程多重交叉验证残差及其协方差的快速计算
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-05-17 DOI: 10.1080/10618600.2024.2353633
David Ginsbourger, Cédric Schärer
{"title":"Fast calculation of Gaussian process multiple-fold cross-validation residuals and their covariances","authors":"David Ginsbourger, Cédric Schärer","doi":"10.1080/10618600.2024.2353633","DOIUrl":"https://doi.org/10.1080/10618600.2024.2353633","url":null,"abstract":"We generalize fast Gaussian process leave-one-out formulae to multiple-fold cross-validation, highlighting in turn the covariance structure of cross-validation residuals in simple and universal kri...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091840","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
Fast Variational Inference for Bayesian Factor Analysis in Single and Multi-Study Settings 单项和多项研究中贝叶斯因子分析的快速变量推理
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-05-15 DOI: 10.1080/10618600.2024.2356173
Blake Hansen, Alejandra Avalos-Pacheco, Massimiliano Russo, Roberta De Vito
{"title":"Fast Variational Inference for Bayesian Factor Analysis in Single and Multi-Study Settings","authors":"Blake Hansen, Alejandra Avalos-Pacheco, Massimiliano Russo, Roberta De Vito","doi":"10.1080/10618600.2024.2356173","DOIUrl":"https://doi.org/10.1080/10618600.2024.2356173","url":null,"abstract":"Factors models are commonly used to analyze high-dimensional data in both single-study and multi-study settings. Bayesian inference for such models relies on Markov Chain Monte Carlo (MCMC) methods...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141092014","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
Performance is not enough: the story told by a Rashomon quartet 光有表演是不够的：罗生门四重奏讲述的故事
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-05-14 DOI: 10.1080/10618600.2024.2344616
Przemysław Biecek, Hubert Baniecki, Mateusz Krzyziński, Dianne Cook
{"title":"Performance is not enough: the story told by a Rashomon quartet","authors":"Przemysław Biecek, Hubert Baniecki, Mateusz Krzyziński, Dianne Cook","doi":"10.1080/10618600.2024.2344616","DOIUrl":"https://doi.org/10.1080/10618600.2024.2344616","url":null,"abstract":"The usual goal of supervised learning is to find the best model, the one that optimizes a particular performance measure. However, what if the explanation provided by this model is completely diffe...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091911","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
Fast and Robust Low-Rank Learning over Networks: A Decentralized Matrix Quantile Regression Approach 网络上快速稳健的低级学习：分散式矩阵量化回归方法
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-05-09 DOI: 10.1080/10618600.2024.2353640
Nan Qiao, Canyi Chen
{"title":"Fast and Robust Low-Rank Learning over Networks: A Decentralized Matrix Quantile Regression Approach","authors":"Nan Qiao, Canyi Chen","doi":"10.1080/10618600.2024.2353640","DOIUrl":"https://doi.org/10.1080/10618600.2024.2353640","url":null,"abstract":"Decentralized low-rank learning is an active research domain with extensive practical applications. A common approach to producing low-rank and robust estimations is to employ a combination of the ...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895406","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
Variance-Reduced Stochastic Optimization for Efficient Inference of Hidden Markov Models 用于隐马尔可夫模型高效推理的减方差随机优化技术
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-05-07 DOI: 10.1080/10618600.2024.2350476
Evan Sidrow, Nancy Heckman, Alexandre Bouchard-Côté, Sarah M. E. Fortune, Andrew W. Trites, Marie Auger-Méthé
{"title":"Variance-Reduced Stochastic Optimization for Efficient Inference of Hidden Markov Models","authors":"Evan Sidrow, Nancy Heckman, Alexandre Bouchard-Côté, Sarah M. E. Fortune, Andrew W. Trites, Marie Auger-Méthé","doi":"10.1080/10618600.2024.2350476","DOIUrl":"https://doi.org/10.1080/10618600.2024.2350476","url":null,"abstract":"Hidden Markov models (HMMs) are popular models to identify a finite number of latent states from sequential data. However, fitting them to large data sets can be computationally demanding because m...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141092011","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
Universal inference meets random projections: a scalable test for log-concavity 通用推理与随机投影：对数凹性的可扩展测试
IF 2.4 2区 数学
Journal of Computational and Graphical Statistics Pub Date : 2024-04-25 DOI: 10.1080/10618600.2024.2347338
Robin Dunn, Aditya Gangrade, Larry Wasserman, Aaditya Ramdas
{"title":"Universal inference meets random projections: a scalable test for log-concavity","authors":"Robin Dunn, Aditya Gangrade, Larry Wasserman, Aaditya Ramdas","doi":"10.1080/10618600.2024.2347338","DOIUrl":"https://doi.org/10.1080/10618600.2024.2347338","url":null,"abstract":"Shape constraints yield flexible middle grounds between fully nonparametric and fully parametric approaches to modeling distributions of data. The specific assumption of log-concavity is motivated ...","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091912","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信