Pub Date : 2023-09-08DOI: 10.1080/00031305.2023.2257253
Jesse Frey, Yimin Zhang
{"title":"Melded Confidence Intervals Do Not Provide Guaranteed Coverage","authors":"Jesse Frey, Yimin Zhang","doi":"10.1080/00031305.2023.2257253","DOIUrl":"https://doi.org/10.1080/00031305.2023.2257253","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125949352","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}
Pub Date : 2023-08-21DOI: 10.1080/00031305.2023.2249965
Hsin-wen Chang, Shu-Hsiang Wang
{"title":"Bivariate Analysis of Distribution Functions Under Biased Sampling","authors":"Hsin-wen Chang, Shu-Hsiang Wang","doi":"10.1080/00031305.2023.2249965","DOIUrl":"https://doi.org/10.1080/00031305.2023.2249965","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117040764","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}
Pub Date : 2023-08-21DOI: 10.1080/00031305.2023.2249967
Sachin S. Pandya, X. Li, Eric Barón, T. Moore
{"title":"Bayesian Detection of Bias in Peremptory Challenges Using Historical Strike Data","authors":"Sachin S. Pandya, X. Li, Eric Barón, T. Moore","doi":"10.1080/00031305.2023.2249967","DOIUrl":"https://doi.org/10.1080/00031305.2023.2249967","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125325324","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}
Pub Date : 2023-08-21DOI: 10.1080/00031305.2023.2250399
Chixiang Chen, Shuo Chen, Qi Long, Sudeshna Das, Ming Wang
{"title":"Multiple-model-based robust estimation of causal treatment effect on a binary outcome with integrated information from secondary outcomes","authors":"Chixiang Chen, Shuo Chen, Qi Long, Sudeshna Das, Ming Wang","doi":"10.1080/00031305.2023.2250399","DOIUrl":"https://doi.org/10.1080/00031305.2023.2250399","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116150597","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}
Pub Date : 2023-08-18DOI: 10.1080/00031305.2023.2249529
W. Hwang, Lu-Fang Chen, J. Stoklosa
{"title":"Counting the unseen: Estimation of susceptibility proportions in zero-inflated models using a conditional likelihood approach","authors":"W. Hwang, Lu-Fang Chen, J. Stoklosa","doi":"10.1080/00031305.2023.2249529","DOIUrl":"https://doi.org/10.1080/00031305.2023.2249529","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116024022","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}
Pub Date : 2023-08-10DOI: 10.1080/00031305.2023.2244542
J. Cohen
{"title":"First-passage times for random partial sums: Yadrenko’s model for e and beyond","authors":"J. Cohen","doi":"10.1080/00031305.2023.2244542","DOIUrl":"https://doi.org/10.1080/00031305.2023.2244542","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130217269","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}
Pub Date : 2023-07-03DOI: 10.1080/00031305.2022.2087735
Xun Li, Joyee Ghosh, G. Villarini
Abstract In many moderate dimensional applications we have multiple response variables that are associated with a common set of predictors. When the main objective is prediction of the response variables, a natural question is: do multivariate regression models that accommodate dependency among the response variables improve prediction compared to their univariate counterparts? Note that in this article, by univariate versus multivariate regression models we refer to regression models with a single versus multiple response variables, respectively. We assume that under both scenarios, there are multiple covariates. Our question is motivated by an application in climate science, which involves the prediction of multiple metrics that measure the activity, intensity, severity etc. of a hurricane season. Average sea surface temperatures (SSTs) during the hurricane season have been used as predictors for each of these metrics, in separate univariate regression models, in the literature. Since the true SSTs are yet to be observed during prediction, typically their forecasts from multiple climate models are used as predictors. Some climate models have a few missing values so we develop Bayesian univariate/multivariate normal regression models, that can handle missing covariates and variable selection uncertainty. Whether Bayesian multivariate normal regression models improve prediction compared to their univariate counterparts is not clear from the existing literature, and in this work we try to fill this gap.
{"title":"A Comparison of Bayesian Multivariate Versus Univariate Normal Regression Models for Prediction","authors":"Xun Li, Joyee Ghosh, G. Villarini","doi":"10.1080/00031305.2022.2087735","DOIUrl":"https://doi.org/10.1080/00031305.2022.2087735","url":null,"abstract":"Abstract In many moderate dimensional applications we have multiple response variables that are associated with a common set of predictors. When the main objective is prediction of the response variables, a natural question is: do multivariate regression models that accommodate dependency among the response variables improve prediction compared to their univariate counterparts? Note that in this article, by univariate versus multivariate regression models we refer to regression models with a single versus multiple response variables, respectively. We assume that under both scenarios, there are multiple covariates. Our question is motivated by an application in climate science, which involves the prediction of multiple metrics that measure the activity, intensity, severity etc. of a hurricane season. Average sea surface temperatures (SSTs) during the hurricane season have been used as predictors for each of these metrics, in separate univariate regression models, in the literature. Since the true SSTs are yet to be observed during prediction, typically their forecasts from multiple climate models are used as predictors. Some climate models have a few missing values so we develop Bayesian univariate/multivariate normal regression models, that can handle missing covariates and variable selection uncertainty. Whether Bayesian multivariate normal regression models improve prediction compared to their univariate counterparts is not clear from the existing literature, and in this work we try to fill this gap.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114353149","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}
Pub Date : 2023-07-03DOI: 10.1080/00031305.2023.2230758
Din Chen
{"title":"Event History Analysis with R, 2nd ed.","authors":"Din Chen","doi":"10.1080/00031305.2023.2230758","DOIUrl":"https://doi.org/10.1080/00031305.2023.2230758","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114623447","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}
Pub Date : 2023-06-09DOI: 10.1080/00031305.2023.2223582
Preston Biro, S. Walker
{"title":"Play Call Strategies and Modeling for Target Outcomes in Football","authors":"Preston Biro, S. Walker","doi":"10.1080/00031305.2023.2223582","DOIUrl":"https://doi.org/10.1080/00031305.2023.2223582","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132118422","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}
Pub Date : 2023-05-22DOI: 10.1080/00031305.2023.2216247
Biao Zhang
{"title":"Inverse probability weighting estimation in completely randomized experiments","authors":"Biao Zhang","doi":"10.1080/00031305.2023.2216247","DOIUrl":"https://doi.org/10.1080/00031305.2023.2216247","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114912667","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}
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