American Statistician最新文献

The Loser’s Curse and the Critical Role of the Utility Function 失败者的诅咒与效用函数的关键作用

IF 1.8 4区数学

American Statistician Pub Date : 2025-05-16 DOI: 10.1080/00031305.2025.2505512

Ryan S. Brill, Abraham J. Wyner

{"title":"The Loser’s Curse and the Critical Role of the Utility Function","authors":"Ryan S. Brill, Abraham J. Wyner","doi":"10.1080/00031305.2025.2505512","DOIUrl":"https://doi.org/10.1080/00031305.2025.2505512","url":null,"abstract":"A longstanding question in the judgment and decision making literature is whether experts, even in high-stakes environments, exhibit the same cognitive biases observed in controlled experiments with inexperienced participants. Massey and Thaler (2013) claim to have found an example of bias and irrationality in expert decision making: general managers’ behavior in the National Football League draft pick trade market. They argue that general managers systematically overvalue top draft picks, which generate less surplus value on average than later first-round picks, a phenomenon known as the loser’s curse. Their conclusion hinges on the assumption that general managers should use expected surplus value as their utility function for evaluating draft picks. This assumption, however, is neither explicitly justified nor necessarily aligned with the strategic complexities of constructing a National Football League roster. In this paper, we challenge their framework by considering alternative utility functions, particularly those that emphasize the acquisition of transformational players––those capable of dramatically increasing a team’s chances of winning the Super Bowl. Under a decision rule that prioritizes the probability of acquiring elite players, which we construct from a novel Bayesian hierarchical Beta regression model, general managers’ draft trade behavior appears rational rather than systematically flawed. More broadly, our findings highlight the critical role of carefully specifying a utility function when evaluating the quality of decisions.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"16 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066829","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}

引用次数: 0

High Dimensional Space Oddity 高维空间奇度

IF 1.8 4区数学

American Statistician Pub Date : 2025-05-15 DOI: 10.1080/00031305.2025.2505507

Haim Bar, Vladimir Pozdnyakov

引用次数: 0

Bayesian Inference and the Principle of Maximum Entropy 贝叶斯推理与最大熵原理

IF 1.8 4区数学

American Statistician Pub Date : 2025-05-06 DOI: 10.1080/00031305.2025.2501799

Duncan K. Foley, Ellis Scharfenaker

{"title":"Bayesian Inference and the Principle of Maximum Entropy","authors":"Duncan K. Foley, Ellis Scharfenaker","doi":"10.1080/00031305.2025.2501799","DOIUrl":"https://doi.org/10.1080/00031305.2025.2501799","url":null,"abstract":"Bayes’ theorem incorporates distinct types of information through the likelihood and prior. Direct observations of state variables enter the likelihood and modify posterior probabilities through consistent updating. Information in terms of expected values of state variables modify posterior probabilities by constraining prior probabilities to be consistent with the information. Constraints on the prior can be exact, limiting hypothetical frequency distributions to only those that satisfy the constraints, or be approximate, allowing residual deviations from the exact constraint to some degree of tolerance. When the model parameters and constraint tolerances are known, posterior probabilities follow directly from Bayes’ theorem. When parameters and tolerances are unknown a prior for them must be specified. When the system is close to statistical equilibrium the computation of posterior probabilities is simplified due to the concentration of the prior on the maximum entropy hypothesis. The relationship between maximum entropy reasoning and Bayes’ theorem from this point of view is that maximum entropy reasoning is a special case of Bayesian inference with a constrained entropy-favoring prior.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"4 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933216","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}

引用次数: 0

Much Ado About Survey Tables: A Comparison of Chi-Square Tests and Software to Analyze Categorical Survey Data 调查表格：卡方检验与软件分析分类调查数据的比较

IF 1.8 4区数学

American Statistician Pub Date : 2025-05-05 DOI: 10.1080/00031305.2025.2501800

Li-Yen R. Hu, Yulei He, Katherine E. Irimata, Vladislav Beresovsky

{"title":"Much Ado About Survey Tables: A Comparison of Chi-Square Tests and Software to Analyze Categorical Survey Data","authors":"Li-Yen R. Hu, Yulei He, Katherine E. Irimata, Vladislav Beresovsky","doi":"10.1080/00031305.2025.2501800","DOIUrl":"https://doi.org/10.1080/00031305.2025.2501800","url":null,"abstract":"Chi-square tests are often employed to examine the association of categorical variables, the homogeneity of proportions between two or more samples, and the goodness-of-fit for a specified distribution. To account for the complex design of survey data, variants of chi-square tests as well as software packages that implement these tests have been developed. Nevertheless, from a survey practitioner’s perspective, there is a lack of applied literature that reviews and compares alternative options of survey chi-square tests and their associated programming and output. This paper aims to fill such a gap.Many modern statistical software packages for survey analysis are capable of computing either the Wald chi-square test or the Rao-Scott chi-square test, along with other types of chi-square tests, including the Rao-Scott likelihood ratio chi-square test and the Wald log-linear chi-square test. This paper focuses on these four types of chi-square tests, and examines four statistical packages that compute them in SAS®, R, Python and SUDAAN<sup>®</sup>. While the same type of tests using different packages yield similar results, different types of chi-square tests may yield variations in p-values when conducting the same comparison. Sample programming code is included in Appendix for readers’ reference.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"34 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933218","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}

引用次数: 0

Analytics, Have Some Humility: A Statistical View of Fourth-Down Decision Making 分析，要谦虚：第四回合决策的统计观点

IF 1.8 4区数学

American Statistician Pub Date : 2025-04-18 DOI: 10.1080/00031305.2025.2475801

Ryan S. Brill, Ronald Yurko, Abraham J. Wyner

引用次数: 0

Play-by-Play Volleyball Win Probability Model 逐场排球获胜概率模型

IF 1.8 4区数学

American Statistician Pub Date : 2025-04-10 DOI: 10.1080/00031305.2025.2490786

Nathan Hawkins, Gilbert W. Fellingham, Garritt L. Page

引用次数: 0

Learn R: As a Language, 2nd ed. 《学习R：作为一门语言》第二版。

IF 1.8 4区数学

American Statistician Pub Date : 2025-04-09 DOI: 10.1080/00031305.2025.2490305

Haihan Yu

引用次数: 0

Data Science in Practice. 数据科学实践。

IF 1.8 4区数学

American Statistician Pub Date : 2025-04-09 DOI: 10.1080/00031305.2025.2490304

Xiao Hui Tai

引用次数: 0

An Example to Illustrate Randomized Trial Estimands and Estimators 一个说明随机试验估计和估计的例子

IF 1.8 4区数学

American Statistician Pub Date : 2025-04-05 DOI: 10.1080/00031305.2025.2468399

Linda J. Harrison, Sean S. Brummel

{"title":"An Example to Illustrate Randomized Trial Estimands and Estimators","authors":"Linda J. Harrison, Sean S. Brummel","doi":"10.1080/00031305.2025.2468399","DOIUrl":"https://doi.org/10.1080/00031305.2025.2468399","url":null,"abstract":"Recently, the International Conference on Harmonisation finalized an estimand framework for randomized trials that was adopted by regulatory bodies worldwide. The framework introduced five strategies for handling post-randomization events; namely the treatment policy, composite variable, while on treatment, hypothetical and principal stratum estimands. We describe an illustrative example to elucidate the difference between these five strategies for handling intercurrent events and provide an estimation technique for each. Specifically, we consider the intercurrent event of treatment discontinuation and introduce potential outcome notation to describe five estimands and corresponding estimators: (1) an intention-to-treat estimator of the total effect of a treatment policy; (2) an intention-to-treat estimator of a composite of the outcome and remaining on treatment; (3) a per-protocol estimator of the outcome in individuals observed to remain on treatment; (4) a g-computation estimator of a hypothetical scenario that all individuals remain on treatment; and (5) a principal stratum estimator of the treatment effect in individuals who would remain on treatment under the experimental condition. Additional insight is provided by defining situations where certain estimands are equal, and by studying the while on treatment strategy under repeated outcome measures. We highlight relevant causal inference literature to enable adoption in practice.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"27 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933336","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}

引用次数: 0

Applied Machine Learning Using mlr3 in R 在R中使用mlr3应用机器学习

IF 1.8 4区数学

American Statistician Pub Date : 2025-04-03 DOI: 10.1080/00031305.2025.2469923

Xueying Tang

引用次数: 0