The American Statistician最新文献_第7页

Statistical Guidance to Authors at Top-Ranked Journals across Scientific Disciplines 对各学科顶级期刊作者的统计指导

The American Statistician Pub Date : 2022-11-08 DOI: 10.1080/00031305.2022.2143897

T. Hardwicke, M. Salholz-Hillel, M. Malički, Dénes Szűcs, Theiss Bendixen, J. Ioannidis

{"title":"Statistical Guidance to Authors at Top-Ranked Journals across Scientific Disciplines","authors":"T. Hardwicke, M. Salholz-Hillel, M. Malički, Dénes Szűcs, Theiss Bendixen, J. Ioannidis","doi":"10.1080/00031305.2022.2143897","DOIUrl":"https://doi.org/10.1080/00031305.2022.2143897","url":null,"abstract":"Abstract Scientific journals may counter the misuse, misreporting, and misinterpretation of statistics by providing guidance to authors. We described the nature and prevalence of statistical guidance at 15 journals (top-ranked by Impact Factor) in each of 22 scientific disciplines across five high-level domains (N = 330 journals). The frequency of statistical guidance varied across domains (Health & Life Sciences: 122/165 journals, 74%; Multidisciplinary: 9/15 journals, 60%; Social Sciences: 8/30 journals, 27%; Physical Sciences: 21/90 journals, 23%; Formal Sciences: 0/30 journals, 0%). In one discipline (Clinical Medicine), statistical guidance was provided by all examined journals and in two disciplines (Mathematics and Computer Science) no examined journals provided statistical guidance. Of the 160 journals providing statistical guidance, 93 had a dedicated statistics section in their author instructions. The most frequently mentioned topics were confidence intervals (90 journals) and p-values (88 journals). For six “hotly debated” topics (statistical significance, p-values, Bayesian statistics, effect sizes, confidence intervals, and sample size planning/justification) journals typically offered implicit or explicit endorsement and rarely provided opposition. The heterogeneity of statistical guidance provided by top-ranked journals within and between disciplines highlights a need for further research and debate about the role journals can play in improving statistical practice.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132429807","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}

引用次数: 5

Mixture of Networks for Clustering Categorical Data: A Penalized Composite Likelihood Approach 分类数据聚类的混合网络:一种惩罚复合似然方法

The American Statistician Pub Date : 2022-11-03 DOI: 10.1080/00031305.2022.2141856

Jangsun Baek, Jeong‐Soo Park

{"title":"Mixture of Networks for Clustering Categorical Data: A Penalized Composite Likelihood Approach","authors":"Jangsun Baek, Jeong‐Soo Park","doi":"10.1080/00031305.2022.2141856","DOIUrl":"https://doi.org/10.1080/00031305.2022.2141856","url":null,"abstract":"Abstract One of the challenges in clustering categorical data is the curse of dimensionality caused by the inherent sparsity of high-dimensional data, the records of which include a large number of attributes. The latent class model (LCM) assumes local independence between the variables in clusters, and is a parsimonious model-based clustering approach that has been used to circumvent the problem. The mixture of a log-linear model is more flexible but requires more parameters to be estimated. In this research, we recognize that each categorical observation can be conceived as a network with pairwise linked nodes, which are the response levels of the observation attributes. Therefore, the categorical data for clustering is considered a finite mixture of different component layer networks with distinct patterns. We apply a penalized composite likelihood approach to a finite mixture of networks for sparse multivariate categorical data to reduce the number of parameters, implement the EM algorithm to estimate the model parameters, and show that the estimates are consistent and satisfy asymptotic normality. The performance of the proposed approach is shown to be better in comparison with the conventional methods for both synthetic and real datasets.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124637559","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}

引用次数: 0

A Case for Nonparametrics 非参数的一种情况

The American Statistician Pub Date : 2022-11-03 DOI: 10.1080/00031305.2022.2141858

Roy Bower, Justin Hager, Chris Cherniakov, Samay Gupta, William Cipolli

引用次数: 2

Probability, Statistics, and Data: A Fresh Approach Using R 概率，统计和数据:使用R的新方法

The American Statistician Pub Date : 2022-10-02 DOI: 10.1080/00031305.2022.2126684

Scott A. Roths

引用次数: 0

Statistical Issues in Drug Development, 3rd ed. 药物开发中的统计问题，第3版。

The American Statistician Pub Date : 2022-10-02 DOI: 10.1080/00031305.2022.2126685

J. Cui, H. Fu

引用次数: 7

The State of Play of Reproducibility in Statistics: An Empirical Analysis 统计学中再现性的现状:一个实证分析

The American Statistician Pub Date : 2022-09-30 DOI: 10.1080/00031305.2022.2131625

Xin Xiong, Ivor Cribben

{"title":"The State of Play of Reproducibility in Statistics: An Empirical Analysis","authors":"Xin Xiong, Ivor Cribben","doi":"10.1080/00031305.2022.2131625","DOIUrl":"https://doi.org/10.1080/00031305.2022.2131625","url":null,"abstract":"Abstract Reproducibility, the ability to reproduce the results of published papers or studies using their computer code and data, is a cornerstone of reliable scientific methodology. Studies where results cannot be reproduced by the scientific community should be treated with caution. Over the past decade, the importance of reproducible research has been frequently stressed in a wide range of scientific journals such as Nature and Science and international magazines such as The Economist. However, multiple studies have demonstrated that scientific results are often not reproducible across research areas such as psychology and medicine. Statistics, the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data, prides itself on its openness when it comes to sharing both computer code and data. In this article, we examine reproducibility in the field of statistics by attempting to reproduce the results in 93 published papers in prominent journals using functional magnetic resonance imaging (fMRI) data during the 2010–2021 period. Overall, from both the computer code and the data perspective, among all the 93 examined papers, we could only reproduce the results in 14 (15.1%) papers, that is, the papers provide both executable computer code (or software) with the real fMRI data, and our results matched the results in the paper. Finally, we conclude with some author-specific and journal-specific recommendations to improve the research reproducibility in statistics.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127833819","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}

引用次数: 3

Athlete Recruitment and the Myth of the Sophomore Peak 运动员招募与高二高峰神话

The American Statistician Pub Date : 2022-09-27 DOI: 10.1080/00031305.2022.2127896

M. McGee, Benjamin Williams, Jacy Sparks

{"title":"Athlete Recruitment and the Myth of the Sophomore Peak","authors":"M. McGee, Benjamin Williams, Jacy Sparks","doi":"10.1080/00031305.2022.2127896","DOIUrl":"https://doi.org/10.1080/00031305.2022.2127896","url":null,"abstract":"Abstract Conventional wisdom dispersed by fans and coaches in the stands at almost any high school track meet suggests female athletes typically peak around 10th grade or earlier (15 years of age), particularly for distance runners, and male athletes continuously improve. Given that universities in the United States typically recruit track and field athletes from high school teams, it is important to understand the age of peak performance at the high school level. Athletes are often recruited starting in their sophomore year of high school and individuals develop at different rates during adolescence; however, the individual development factor is usually not taken into account during recruitment. In this study, we curate data on event times for high school track and field athletes from the years 2011 to 2019 to determine the trajectory of fastest times for male and female athletes in the 200m, 400m, 800m, and 1600m races. We show, through visualizations and models, that, for most athletes, the sophomore peak is a myth. Performance is mostly dependent on the individual athlete. That said, the trajectories cluster into four or five types, depending on the race distance. We explain the significance of the types for future recruitment.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117213132","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}

引用次数: 0

Bayesian-Frequentist Hybrid Inference in Applications with Small Sample Sizes 小样本量应用中的贝叶斯-频率混合推理

The American Statistician Pub Date : 2022-09-23 DOI: 10.1080/00031305.2022.2127897

Gang Han, T. Santner, Haiqun Lin, Ao Yuan

引用次数: 0

Hitting a Prime in 2.43 Dice Rolls (On Average) 平均掷2.43次骰子命中Prime

The American Statistician Pub Date : 2022-09-16 DOI: 10.1080/00031305.2023.2179664

N. Alon, Y. Malinovsky

引用次数: 2

Forbidden Knowledge and Specialized Training: A Versatile Solution for the Two Main Sources of Overfitting in Linear Regression 禁忌知识和专门训练:线性回归中两个主要过拟合来源的通用解决方案

The American Statistician Pub Date : 2022-09-03 DOI: 10.1080/00031305.2022.2128874

Chris Rohlfs

引用次数: 2