Pub Date : 2025-07-30DOI: 10.1080/00031305.2025.2539998
Andrey Skripnikov, Sujit Sivadanam
{"title":"American Football Scores: Using Partially Regularized Ordinal Regression to Adjust for Strength of Opponents, Within-Team Complementary Unit Performance","authors":"Andrey Skripnikov, Sujit Sivadanam","doi":"10.1080/00031305.2025.2539998","DOIUrl":"https://doi.org/10.1080/00031305.2025.2539998","url":null,"abstract":"","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"13 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144747264","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 : 2025-07-29DOI: 10.1080/00031305.2025.2539241
Paul Sheridan, Zeyad Ahmed, Aitazaz A. Farooque
{"title":"A Fisher’s exact test justification of the TF–IDF term-weighting scheme","authors":"Paul Sheridan, Zeyad Ahmed, Aitazaz A. Farooque","doi":"10.1080/00031305.2025.2539241","DOIUrl":"https://doi.org/10.1080/00031305.2025.2539241","url":null,"abstract":"","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"27 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144737008","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 : 2025-07-28DOI: 10.1080/00031305.2025.2539235
Peter J. Rousseeuw
{"title":"Explainable Linear and Generalized Linear Models by the Predictions Plot","authors":"Peter J. Rousseeuw","doi":"10.1080/00031305.2025.2539235","DOIUrl":"https://doi.org/10.1080/00031305.2025.2539235","url":null,"abstract":"","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"63 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144715456","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 : 2025-07-23DOI: 10.1080/00031305.2025.2537055
Fernando Rodriguez Avellaneda, Erick A. Chacón-Montalván, Paula Moraga
{"title":"Multivariate disaggregation modeling of air pollutants: a case-study of PM2.5, PM10 and ozone prediction in Portugal and Italy","authors":"Fernando Rodriguez Avellaneda, Erick A. Chacón-Montalván, Paula Moraga","doi":"10.1080/00031305.2025.2537055","DOIUrl":"https://doi.org/10.1080/00031305.2025.2537055","url":null,"abstract":"","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"18 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144693825","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 : 2025-07-07DOI: 10.1080/00031305.2025.2527319
Kwun Chuen Gary Chan
{"title":"A First Course in Causal Inference.","authors":"Kwun Chuen Gary Chan","doi":"10.1080/00031305.2025.2527319","DOIUrl":"https://doi.org/10.1080/00031305.2025.2527319","url":null,"abstract":"","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"5 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144577847","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 : 2025-07-01DOI: 10.1080/00031305.2025.2526545
Md. Belal Hossain, Mohsen Sadatsafavi, James C. Johnston, Hubert Wong, Victoria J. Cook, Mohammad Ehsanul Karim
{"title":"LASSO-based Survival Prediction Modelling with Multiply Imputed Data: A Case Study in Tuberculosis Mortality Prediction","authors":"Md. Belal Hossain, Mohsen Sadatsafavi, James C. Johnston, Hubert Wong, Victoria J. Cook, Mohammad Ehsanul Karim","doi":"10.1080/00031305.2025.2526545","DOIUrl":"https://doi.org/10.1080/00031305.2025.2526545","url":null,"abstract":"","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"13 1","pages":"1-20"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533235","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 : 2025-06-04DOI: 10.1080/00031305.2025.2515869
Owen Fiore, Elizabeth D. Schifano, Jun Yan
Devon Allen’s disqualification at the men’s 110-meter hurdle final at the 2022 World Track and Field Championships, due to a reaction time (RT) of 0.099 seconds—just 0.001 seconds below the allowable threshold—sparked widespread debate over the fairness and validity of RT rules. This study investigates two key issues: variations in timing systems and the justification for the 0.1-second disqualification threshold. We pooled RT data from men’s 110-meter hurdles and 100-meter dash, as well as women’s 100-meter hurdles and 100-meter dash, spanning national and international competitions. Using a rank-sum test for clustered data, we compared RTs across multiple competitions, while a generalized Gamma model with random effects for venue and heat was applied to evaluate the threshold. Our analyses reveal significant differences in RTs between the 2022 World Championships and other competitions, pointing to systematic variations in timing systems. Additionally, the model shows that RTs below 0.1 seconds, though rare, are physiologically plausible. These findings highlight the need for standardized timing protocols and a re-evaluation of the 0.1-second disqualification threshold to promote fairness in elite competition.
{"title":"On Devon Allen’s Disqualification at the 2022 World Track and Field Championships","authors":"Owen Fiore, Elizabeth D. Schifano, Jun Yan","doi":"10.1080/00031305.2025.2515869","DOIUrl":"https://doi.org/10.1080/00031305.2025.2515869","url":null,"abstract":"Devon Allen’s disqualification at the men’s 110-meter hurdle final at the 2022 World Track and Field Championships, due to a reaction time (RT) of 0.099 seconds—just 0.001 seconds below the allowable threshold—sparked widespread debate over the fairness and validity of RT rules. This study investigates two key issues: variations in timing systems and the justification for the 0.1-second disqualification threshold. We pooled RT data from men’s 110-meter hurdles and 100-meter dash, as well as women’s 100-meter hurdles and 100-meter dash, spanning national and international competitions. Using a rank-sum test for clustered data, we compared RTs across multiple competitions, while a generalized Gamma model with random effects for venue and heat was applied to evaluate the threshold. Our analyses reveal significant differences in RTs between the 2022 World Championships and other competitions, pointing to systematic variations in timing systems. Additionally, the model shows that RTs below 0.1 seconds, though rare, are physiologically plausible. These findings highlight the need for standardized timing protocols and a re-evaluation of the 0.1-second disqualification threshold to promote fairness in elite competition.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"12 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219095","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 : 2025-06-04DOI: 10.1080/00031305.2025.2507764
David R. Bickel
A strictly Bayesian model consists of a set of possible data distributions and a prior distribution over that set. If there are other models available, how well they predicted the data may be compared using Bayes factors. If not, a model may be checked using a Bayesian p-value such as a prior predictive p-value or a posterior predictive p-value. However, recent criticisms of ordinary p-values apply with equal force against Bayesian p-values. Many of those criticisms are overcome by e-values, martingales interpreted as the amount of evidence discrediting a null hypothesis, measured as a payoff for betting against it.This paper proposes the use of e-values to check Bayesian models by testing their prior predictive distributions as null hypotheses. Two generally applicable methods for checking strictly Bayesian models are provided. The first method calibrates Bayesian p-values by transforming them into Bayesian e-values. The second method uses Bayes factors or their approximations as Bayesian e-values.A robust Bayesian model, a set of strictly Bayesian models, may be checked using various functions that use the e-values of those strictly Bayesian models. Other functions measure how much the data support a Bayesian model. Relations to possibility theory are discussed.
{"title":"Bayesian model checking by betting: A game-theoretic alternative to Bayesian p -values and classical Bayes factors","authors":"David R. Bickel","doi":"10.1080/00031305.2025.2507764","DOIUrl":"https://doi.org/10.1080/00031305.2025.2507764","url":null,"abstract":"A strictly Bayesian model consists of a set of possible data distributions and a prior distribution over that set. If there are other models available, how well they predicted the data may be compared using Bayes factors. If not, a model may be checked using a Bayesian <i>p</i>-value such as a prior predictive <i>p</i>-value or a posterior predictive <i>p</i>-value. However, recent criticisms of ordinary <i>p</i>-values apply with equal force against Bayesian <i>p</i>-values. Many of those criticisms are overcome by <i>e</i>-values, martingales interpreted as the amount of evidence discrediting a null hypothesis, measured as a payoff for betting against it.This paper proposes the use of <i>e</i>-values to check Bayesian models by testing their prior predictive distributions as null hypotheses. Two generally applicable methods for checking strictly Bayesian models are provided. The first method calibrates Bayesian <i>p</i>-values by transforming them into Bayesian <i>e</i>-values. The second method uses Bayes factors or their approximations as Bayesian <i>e</i>-values.A robust Bayesian model, a set of strictly Bayesian models, may be checked using various functions that use the <i>e</i>-values of those strictly Bayesian models. Other functions measure how much the data support a Bayesian model. Relations to possibility theory are discussed.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"176 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219094","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 : 2025-05-21DOI: 10.1080/00031305.2025.2509664
Lee Kennedy-Shaffer
The undergraduate curriculum in statistics and data science is undergoing changes to accommodate new methods, newly interested students, and the changing role of statistics in society. Because of this, it is more important than ever that students understand the role of study design and how to formulate meaningful scientific and statistical research questions. While the traditional Design of Experiments course is still extremely valuable for students heading to industry and research careers, a broader study design course that incorporates survey sampling, observational studies, and the basics of causal inference with randomized experiment design is particularly useful for students with a wide range of applied interests. Here, I describe such a course at a small liberal arts college, along with ways to adapt it to meet different student and instructor background and interests. The course serves as a valuable bridge to advanced statistical coursework, meets key statistical literacy and communication learning goals, and can be tailored to the desired level of computational and mathematical fluency. Through reading, discussing, and critiquing actual published research studies, students learn that statistics is a living discipline with real consequences and become better consumers and producers of scientific research and data-driven insights.
{"title":"An Undergraduate Course on the Statistical Principles of Research Study Design","authors":"Lee Kennedy-Shaffer","doi":"10.1080/00031305.2025.2509664","DOIUrl":"https://doi.org/10.1080/00031305.2025.2509664","url":null,"abstract":"The undergraduate curriculum in statistics and data science is undergoing changes to accommodate new methods, newly interested students, and the changing role of statistics in society. Because of this, it is more important than ever that students understand the role of study design and how to formulate meaningful scientific and statistical research questions. While the traditional Design of Experiments course is still extremely valuable for students heading to industry and research careers, a broader study design course that incorporates survey sampling, observational studies, and the basics of causal inference with randomized experiment design is particularly useful for students with a wide range of applied interests. Here, I describe such a course at a small liberal arts college, along with ways to adapt it to meet different student and instructor background and interests. The course serves as a valuable bridge to advanced statistical coursework, meets key statistical literacy and communication learning goals, and can be tailored to the desired level of computational and mathematical fluency. Through reading, discussing, and critiquing actual published research studies, students learn that statistics is a living discipline with real consequences and become better consumers and producers of scientific research and data-driven insights.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"155 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144113683","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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