Pub Date : 2024-03-26DOI: 10.1080/00031305.2024.2333864
Robert Calvert Jump
In this paper, I propose a tractable approach to Bayesian inference in a simple linear regression model for which the standard exogeneity assumption does not hold. By specifying a beta prior for th...
{"title":"Tractable Bayesian inference for an unidentified simple linear regression model","authors":"Robert Calvert Jump","doi":"10.1080/00031305.2024.2333864","DOIUrl":"https://doi.org/10.1080/00031305.2024.2333864","url":null,"abstract":"In this paper, I propose a tractable approach to Bayesian inference in a simple linear regression model for which the standard exogeneity assumption does not hold. By specifying a beta prior for th...","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"13 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140291666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-18DOI: 10.1080/00031305.2024.2332764
Joseph F. Lucke
I present the moments of the nonnegative adjusted estimator of the squared multiple correlation ρ2, the coefficient of determination for random-predictor regression. This estimator, first proposed...
{"title":"Moments of the Nonnegative Adjusted Estimator of Squared Multiple Correlation","authors":"Joseph F. Lucke","doi":"10.1080/00031305.2024.2332764","DOIUrl":"https://doi.org/10.1080/00031305.2024.2332764","url":null,"abstract":"I present the moments of the nonnegative adjusted estimator of the squared multiple correlation ρ2, the coefficient of determination for random-predictor regression. This estimator, first proposed...","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"8 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140192675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-11DOI: 10.1080/00031305.2024.2329681
Yifan Yang, Chixiang Chen, Shuo Chen
Estimating a covariance matrix is central to high-dimensional data analysis. Empirical analyses of high-dimensional biomedical data, including genomics, proteomics, microbiome, and neuroimaging, am...
{"title":"Covariance Matrix Estimation for High-Throughput Biomedical Data with Interconnected Communities","authors":"Yifan Yang, Chixiang Chen, Shuo Chen","doi":"10.1080/00031305.2024.2329681","DOIUrl":"https://doi.org/10.1080/00031305.2024.2329681","url":null,"abstract":"Estimating a covariance matrix is central to high-dimensional data analysis. Empirical analyses of high-dimensional biomedical data, including genomics, proteomics, microbiome, and neuroimaging, am...","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"20 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140114429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-11DOI: 10.1080/00031305.2024.2327535
Minh Nguyen, Tiffany Eulalio, Ben Marafino, Christian Rose, Jonathan H. Chen, Michael Baiocchi
A gap remains between developing risk prediction models and deploying models to support real-world decision making, especially in high-stakes situations. Human-experts’ reasoning abilities remain c...
{"title":"Thick Data Analytics (TDA): An Iterative and Inductive Framework for Algorithmic Improvement","authors":"Minh Nguyen, Tiffany Eulalio, Ben Marafino, Christian Rose, Jonathan H. Chen, Michael Baiocchi","doi":"10.1080/00031305.2024.2327535","DOIUrl":"https://doi.org/10.1080/00031305.2024.2327535","url":null,"abstract":"A gap remains between developing risk prediction models and deploying models to support real-world decision making, especially in high-stakes situations. Human-experts’ reasoning abilities remain c...","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"21 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140188718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-21DOI: 10.1080/00031305.2024.2319182
Jesse Hemerik
There is no consensus on the meaning of the term “randomization test”. Contradictory uses of the term are leading to confusion, misunderstandings and indeed invalid data analyses. A main source of ...
{"title":"On the term “randomization test”","authors":"Jesse Hemerik","doi":"10.1080/00031305.2024.2319182","DOIUrl":"https://doi.org/10.1080/00031305.2024.2319182","url":null,"abstract":"There is no consensus on the meaning of the term “randomization test”. Contradictory uses of the term are leading to confusion, misunderstandings and indeed invalid data analyses. A main source of ...","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"16 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139976693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1080/00031305.2024.2316725
Elliot Dovers, Jakub Stoklosa, David I. Warton
While log-Gaussian Cox process regression models are useful tools for modeling point patterns, they can be technically difficult to fit and require users to learn/adopt bespoke software. We show th...
{"title":"Fitting log-Gaussian Cox processes using generalized additive model software","authors":"Elliot Dovers, Jakub Stoklosa, David I. Warton","doi":"10.1080/00031305.2024.2316725","DOIUrl":"https://doi.org/10.1080/00031305.2024.2316725","url":null,"abstract":"While log-Gaussian Cox process regression models are useful tools for modeling point patterns, they can be technically difficult to fit and require users to learn/adopt bespoke software. We show th...","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"295 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139739435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-05DOI: 10.1080/00031305.2024.2302792
Maria Francesca Marino
Published in The American Statistician (Vol. 78, No. 1, 2024)
发表于《美国统计学家》（第 78 卷第 1 期，2024 年）
{"title":"Applied Linear Regression for Longitudinal Data: With an Emphasis on Missing Observations","authors":"Maria Francesca Marino","doi":"10.1080/00031305.2024.2302792","DOIUrl":"https://doi.org/10.1080/00031305.2024.2302792","url":null,"abstract":"Published in The American Statistician (Vol. 78, No. 1, 2024)","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"15 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139720356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-23DOI: 10.1080/00031305.2024.2308821
Xinkai Zhou, Qiang Heng, Eric C. Chi, Hua Zhou
This paper advocates proximal Markov Chain Monte Carlo (ProxMCMC) as a flexible and general Bayesian inference framework for constrained or regularized estimation. Originally introduced in the Baye...
{"title":"Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation","authors":"Xinkai Zhou, Qiang Heng, Eric C. Chi, Hua Zhou","doi":"10.1080/00031305.2024.2308821","DOIUrl":"https://doi.org/10.1080/00031305.2024.2308821","url":null,"abstract":"This paper advocates proximal Markov Chain Monte Carlo (ProxMCMC) as a flexible and general Bayesian inference framework for constrained or regularized estimation. Originally introduced in the Baye...","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"154 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139544089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1080/00031305.2024.2303416
Chaitanya Joshi, Charné Nel, Javier Cano, Devon L.L. Polaschek
Adversarial Risk Analysis (ARA) allows for much more realistic modeling of game theoretic decision problems than Bayesian game theory. While ARA solutions for various applications have been discuss...
与贝叶斯博弈论相比，对抗性风险分析（ARA）可以对博弈论决策问题进行更真实的建模。虽然针对各种应用的 ARA 解决方案已被讨论过，但仍有许多问题有待解决。
{"title":"Parole Board Decision-Making using Adversarial Risk Analysis","authors":"Chaitanya Joshi, Charné Nel, Javier Cano, Devon L.L. Polaschek","doi":"10.1080/00031305.2024.2303416","DOIUrl":"https://doi.org/10.1080/00031305.2024.2303416","url":null,"abstract":"Adversarial Risk Analysis (ARA) allows for much more realistic modeling of game theoretic decision problems than Bayesian game theory. While ARA solutions for various applications have been discuss...","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"23 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139510783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-10DOI: 10.1080/00031305.2024.2303419
Lauren D. Liao, Yeyi Zhu, Amanda L. Ngo, Rana F. Chehab, Samuel D. Pimentel
Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline varia...
{"title":"Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot","authors":"Lauren D. Liao, Yeyi Zhu, Amanda L. Ngo, Rana F. Chehab, Samuel D. Pimentel","doi":"10.1080/00031305.2024.2303419","DOIUrl":"https://doi.org/10.1080/00031305.2024.2303419","url":null,"abstract":"Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline varia...","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"73 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139407646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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