Pub Date : 2024-05-22DOI: 10.1080/10618600.2024.2353653
Lucas Erlandson, Ana María Estrada Gómez, Edmond Chow, Kamran Paynabar
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...
{"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}
Pub Date : 2024-05-21DOI: 10.1080/10618600.2024.2358156
Xiaoping Shi
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...
{"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}
Pub Date : 2024-05-20DOI: 10.1080/10618600.2024.2357626
Jiří Dvořák, Tomáš Mrkvička
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 ...
{"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}
Pub Date : 2024-05-20DOI: 10.1080/10618600.2024.2357636
Matthew J. Heiner, Garritt L. Page, Fernando Andrés Quintana
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...
{"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}
Pub Date : 2024-05-17DOI: 10.1080/10618600.2024.2353633
David Ginsbourger, Cédric Schärer
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...
我们将快速高斯过程留一公式推广到多倍交叉验证中，反过来在简单和普遍的kri...
{"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}
Pub Date : 2024-05-15DOI: 10.1080/10618600.2024.2356173
Blake Hansen, Alejandra Avalos-Pacheco, Massimiliano Russo, Roberta De Vito
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...
{"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}
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...
{"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}
Pub Date : 2024-05-09DOI: 10.1080/10618600.2024.2353640
Nan Qiao, Canyi Chen
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 ...
分散低阶学习是一个活跃的研究领域，有着广泛的实际应用。产生低秩和稳健估计的一种常见方法是结合...
{"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}
Pub Date : 2024-05-07DOI: 10.1080/10618600.2024.2350476
Evan Sidrow, Nancy Heckman, Alexandre Bouchard-Côté, Sarah M. E. Fortune, Andrew W. Trites, Marie Auger-Méthé
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...
{"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}
Pub Date : 2024-04-25DOI: 10.1080/10618600.2024.2347338
Robin Dunn, Aditya Gangrade, Larry Wasserman, Aaditya Ramdas
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 ...
{"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}
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