{"title":"Heather Battey’s contribution to the Discussion of ‘Parameterizing and Simulating from Causal Models’ by Evans and Didelez’","authors":"H. Battey","doi":"10.1093/jrsssb/qkae019","DOIUrl":"https://doi.org/10.1093/jrsssb/qkae019","url":null,"abstract":"","PeriodicalId":506057,"journal":{"name":"Journal of the Royal Statistical Society Series B: Statistical Methodology","volume":"2 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Richard Guo’s contribution to the Discussion of ‘Parameterizing and Simulating from Causal Models’ by Evans and Didelez’","authors":"F. R. Guo","doi":"10.1093/jrsssb/qkae018","DOIUrl":"https://doi.org/10.1093/jrsssb/qkae018","url":null,"abstract":"","PeriodicalId":506057,"journal":{"name":"Journal of the Royal Statistical Society Series B: Statistical Methodology","volume":"31 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joe Benton, Yuyang Shi, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet
{"title":"Authors' reply to the Discussion of 'From Denoising Diffusions to Denoising Markov Models' at the Discussion Meeting on ‘Probabilistic and statistical aspects of machine learning’","authors":"Joe Benton, Yuyang Shi, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet","doi":"10.1093/jrsssb/qkae010","DOIUrl":"https://doi.org/10.1093/jrsssb/qkae010","url":null,"abstract":"","PeriodicalId":506057,"journal":{"name":"Journal of the Royal Statistical Society Series B: Statistical Methodology","volume":"85 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139863018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joe Benton, Yuyang Shi, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet
{"title":"Authors' reply to the Discussion of 'From Denoising Diffusions to Denoising Markov Models' at the Discussion Meeting on ‘Probabilistic and statistical aspects of machine learning’","authors":"Joe Benton, Yuyang Shi, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet","doi":"10.1093/jrsssb/qkae010","DOIUrl":"https://doi.org/10.1093/jrsssb/qkae010","url":null,"abstract":"","PeriodicalId":506057,"journal":{"name":"Journal of the Royal Statistical Society Series B: Statistical Methodology","volume":"27 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139803357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elynn Y. Chen, Dong Xia, Chencheng Cai, Jianqing Fan
{"title":"Semi-parametric tensor factor analysis by iteratively projected singular value decomposition","authors":"Elynn Y. Chen, Dong Xia, Chencheng Cai, Jianqing Fan","doi":"10.1093/jrsssb/qkae001","DOIUrl":"https://doi.org/10.1093/jrsssb/qkae001","url":null,"abstract":"\u0000 This paper introduces a general framework of Semi-parametric TEnsor Factor Analysis (STEFA) that focuses on the methodology and theory of low-rank tensor decomposition with auxiliary covariates. Semi-parametric TEnsor Factor Analysis models extend tensor factor models by incorporating auxiliary covariates in the loading matrices. We propose an algorithm of iteratively projected singular value decomposition (IP-SVD) for the semi-parametric estimation. It iteratively projects tensor data onto the linear space spanned by the basis functions of covariates and applies singular value decomposition on matricized tensors over each mode. We establish the convergence rates of the loading matrices and the core tensor factor. The theoretical results only require a sub-exponential noise distribution, which is weaker than the assumption of sub-Gaussian tail of noise in the literature. Compared with the Tucker decomposition, IP-SVD yields more accurate estimators with a faster convergence rate. Besides estimation, we propose several prediction methods with new covariates based on the STEFA model. On both synthetic and real tensor data, we demonstrate the efficacy of the STEFA model and the IP-SVD algorithm on both the estimation and prediction tasks.","PeriodicalId":506057,"journal":{"name":"Journal of the Royal Statistical Society Series B: Statistical Methodology","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139867582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elynn Y. Chen, Dong Xia, Chencheng Cai, Jianqing Fan
{"title":"Semi-parametric tensor factor analysis by iteratively projected singular value decomposition","authors":"Elynn Y. Chen, Dong Xia, Chencheng Cai, Jianqing Fan","doi":"10.1093/jrsssb/qkae001","DOIUrl":"https://doi.org/10.1093/jrsssb/qkae001","url":null,"abstract":"\u0000 This paper introduces a general framework of Semi-parametric TEnsor Factor Analysis (STEFA) that focuses on the methodology and theory of low-rank tensor decomposition with auxiliary covariates. Semi-parametric TEnsor Factor Analysis models extend tensor factor models by incorporating auxiliary covariates in the loading matrices. We propose an algorithm of iteratively projected singular value decomposition (IP-SVD) for the semi-parametric estimation. It iteratively projects tensor data onto the linear space spanned by the basis functions of covariates and applies singular value decomposition on matricized tensors over each mode. We establish the convergence rates of the loading matrices and the core tensor factor. The theoretical results only require a sub-exponential noise distribution, which is weaker than the assumption of sub-Gaussian tail of noise in the literature. Compared with the Tucker decomposition, IP-SVD yields more accurate estimators with a faster convergence rate. Besides estimation, we propose several prediction methods with new covariates based on the STEFA model. On both synthetic and real tensor data, we demonstrate the efficacy of the STEFA model and the IP-SVD algorithm on both the estimation and prediction tasks.","PeriodicalId":506057,"journal":{"name":"Journal of the Royal Statistical Society Series B: Statistical Methodology","volume":"609 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139807584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jie Li, P. Fearnhead, Piotr Fryzlewicz, Tengyao Wang
{"title":"Authors’ reply to the Discussion of ‘Automatic Change-Point Detection in Time Series via Deep Learning’ at the Discussion Meeting on ‘Probabilistic and statistical aspects of machine learning’","authors":"Jie Li, P. Fearnhead, Piotr Fryzlewicz, Tengyao Wang","doi":"10.1093/jrsssb/qkae008","DOIUrl":"https://doi.org/10.1093/jrsssb/qkae008","url":null,"abstract":"","PeriodicalId":506057,"journal":{"name":"Journal of the Royal Statistical Society Series B: Statistical Methodology","volume":"54 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140489973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Tamás P. Papp, Paul Fearnhead and Chris Sherlock's contribution to the discussion of “the Discussion Meeting on Probabilistic and statistical aspects of machine learning”","authors":"Tamás P Papp, P. Fearnhead, Chris Sherlock","doi":"10.1093/jrsssb/qkae006","DOIUrl":"https://doi.org/10.1093/jrsssb/qkae006","url":null,"abstract":"","PeriodicalId":506057,"journal":{"name":"Journal of the Royal Statistical Society Series B: Statistical Methodology","volume":"114 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139616183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Johannes Schmidt-Hieber’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’","authors":"Johannes Schmidt-Hieber","doi":"10.1093/jrsssb/qkae007","DOIUrl":"https://doi.org/10.1093/jrsssb/qkae007","url":null,"abstract":"","PeriodicalId":506057,"journal":{"name":"Journal of the Royal Statistical Society Series B: Statistical Methodology","volume":" 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139624025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Integrative conformal p-values for out-of-distribution testing with labelled outliers","authors":"Zi-Chen Liang, Matteo Sesia, Wenguang Sun","doi":"10.1093/jrsssb/qkad138","DOIUrl":"https://doi.org/10.1093/jrsssb/qkad138","url":null,"abstract":"\u0000 This paper presents a conformal inference method for out-of-distribution testing that leverages side information from labelled outliers, which are commonly underutilized or even discarded by conventional conformal p-values. This solution is practical and blends inductive and transductive inference strategies to adaptively weight conformal p-values, while also automatically leveraging the most powerful model from a collection of one-class and binary classifiers. Further, this approach leads to rigorous false discovery rate control in multiple testing when combined with a conditional calibration strategy. Extensive numerical simulations show that the proposed method outperforms existing approaches.","PeriodicalId":506057,"journal":{"name":"Journal of the Royal Statistical Society Series B: Statistical Methodology","volume":"8 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}