American Statistician最新文献_第6页

Bayesian Causality. 贝叶斯因果关系。

IF 1.8 4区数学

American Statistician Pub Date : 2020-01-01 Epub Date: 2019-08-26 DOI: 10.1080/00031305.2019.1647876

Pierre Baldi, Babak Shahbaba

引用次数: 3

The Role of Expert Judgment in Statistical Inference and Evidence-Based Decision-Making. 专家判断在统计推断和循证决策中的作用。

IF 1.8 4区数学

American Statistician Pub Date : 2019-03-20 eCollection Date: 2019-01-01 DOI: 10.1080/00031305.2018.1529623

Naomi C Brownstein, Thomas A Louis, Anthony O'Hagan, Jane Pendergast

引用次数: 0

The New Statistics for Better Science: Ask How Much, How Uncertain, and What Else is Known. 更好的科学新统计:问多少，有多不确定，还有什么是已知的。

IF 1.8 4区数学

American Statistician Pub Date : 2019-01-01 DOI: 10.1080/00031305.2018.1518266

Robert J Calin-Jageman, Geoff Cumming

引用次数: 99

Evidence from marginally significant t statistics. 来自边际显著性统计的证据。

IF 1.8 4区数学

American Statistician Pub Date : 2019-01-01 Epub Date: 2019-03-20 DOI: 10.1080/00031305.2018.1518788

Valen E Johnson

{"title":"Evidence from marginally significant t statistics.","authors":"Valen E Johnson","doi":"10.1080/00031305.2018.1518788","DOIUrl":"https://doi.org/10.1080/00031305.2018.1518788","url":null,"abstract":"This article examines the evidence contained in t statistics that are marginally significant in 5% tests. The bases for evaluating evidence are likelihood ratios and integrated likelihood ratios, computed under a variety of assumptions regarding the alternative hypotheses in null hypothesis significance tests. Likelihood ratios and integrated likelihood ratios provide a useful measure of the evidence in favor of competing hypotheses because they can be interpreted as representing the ratio of the probabilities that each hypothesis assigns to observed data. When they are either very large or very small, they suggest that one hypothesis is much better than the other in predicting observed data. If they are close to 1.0, then both hypotheses provide approximately equally valid explanations for observed data. I find that p-values that are close to 0.05 (i.e., that are \"marginally significant\") correspond to integrated likelihood ratios that are bounded by approximately 7 in two-sided tests, and by approximately 4 in one-sided tests. The modest magnitude of integrated likelihood ratios corresponding to p-values close to 0.05 clearly suggests that higher standards of evidence are needed to support claims of novel discoveries and new effects.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":" ","pages":"129-134"},"PeriodicalIF":1.8,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/00031305.2018.1518788","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37268013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Power and Sample Size for Fixed-Effects Inference in Reversible Linear Mixed Models. 可逆线性混合模型中固定效应推断的功率和样本量。

IF 1.8 4区数学

American Statistician Pub Date : 2019-01-01 Epub Date: 2018-06-04 DOI: 10.1080/00031305.2017.1415972

Yueh-Yun Chi, Deborah H Glueck, Keith E Muller

{"title":"Power and Sample Size for Fixed-Effects Inference in Reversible Linear Mixed Models.","authors":"Yueh-Yun Chi, Deborah H Glueck, Keith E Muller","doi":"10.1080/00031305.2017.1415972","DOIUrl":"https://doi.org/10.1080/00031305.2017.1415972","url":null,"abstract":"Despite the popularity of the general linear mixed model for data analysis, power and sample size methods and software are not generally available for commonly used test statistics and reference distributions. Statisticians resort to simulations with homegrown and uncertified programs or rough approximations which are misaligned with the data analysis. For a wide range of designs with longitudinal and clustering features, we provide accurate power and sample size approximations for inference about fixed effects in linear models we call reversible. We show that under widely applicable conditions, the general linear mixed-model Wald test has non-central distributions equivalent to well-studied multivariate tests. In turn, exact and approximate power and sample size results for the multivariate Hotelling-Lawley test provide exact and approximate power and sample size results for the mixed-model Wald test. The calculations are easily computed with a free, open-source product that requires only a web browser to use. Commercial software can be used for a smaller range of reversible models. Simple approximations allow accounting for modest amounts of missing data. A real-world example illustrates the methods. Sample size results are presented for a multicenter study on pregnancy. The proposed study, an extension of a funded project, has clustering within clinic. Exchangeability among participants allows averaging across them to remove the clustering structure. The resulting simplified design is a single level longitudinal study. Multivariate methods for power provide an approximate sample size. All proofs and inputs for the example are in the Supplementary Materials (available online).","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"73 4","pages":"350-359"},"PeriodicalIF":1.8,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/00031305.2017.1415972","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37631108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Testing for positive quadrant dependence. 正象限相关性检验。

IF 1.8 4区数学

American Statistician Pub Date : 2019-01-01 Epub Date: 2019-05-30 DOI: 10.1080/00031305.2019.1607554

Chuan-Fa Tang, Dewei Wang, Hammou El Barmi, Joshua M Tebbs

引用次数: 5

Limitations of P-Values and R-squared for Stepwise Regression Building: A Fairness Demonstration in Health Policy Risk Adjustment. 逐步回归构建的p值和r平方的局限性:卫生政策风险调整的公平性论证。

IF 1.8 4区数学

American Statistician Pub Date : 2019-01-01 Epub Date: 2019-03-20 DOI: 10.1080/00031305.2018.1518269

Sherri Rose, Thomas G McGuire

{"title":"Limitations of P-Values and R-squared for Stepwise Regression Building: A Fairness Demonstration in Health Policy Risk Adjustment.","authors":"Sherri Rose, Thomas G McGuire","doi":"10.1080/00031305.2018.1518269","DOIUrl":"https://doi.org/10.1080/00031305.2018.1518269","url":null,"abstract":"Stepwise regression building procedures are commonly used applied statistical tools, despite their well-known drawbacks. While many of their limitations have been widely discussed in the literature, other aspects of the use of individual statistical fit measures, especially in high-dimensional stepwise regression settings, have not. Giving primacy to individual fit, as is done with p-values and R2, when group fit may be the larger concern, can lead to misguided decision making. One of the most consequential uses of stepwise regression is in health care, where these tools allocate hundreds of billions of dollars to health plans enrolling individuals with different predicted health care costs. The main goal of this \"risk adjustment\" system is to convey incentives to health plans such that they provide health care services fairly, a component of which is not to discriminate in access or care for persons or groups likely to be expensive. We address some specific limitations of p-values and R2 for high-dimensional stepwise regression in this policy problem through an illustrated example by additionally considering a group-level fairness metric.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":" ","pages":"152-156"},"PeriodicalIF":1.8,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/00031305.2018.1518269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37122994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 23

Comparing Objective and Subjective Bayes Factors for the Two-Sample Comparison: The Classification Theorem in Action. 比较两个样本比较的客观和主观贝叶斯因素：分类定理在行动。

IF 1.8 4区数学

American Statistician Pub Date : 2019-01-01 Epub Date: 2018-05-10 DOI: 10.1080/00031305.2017.1322142

Mithat Gönen, Wesley O Johnson, Yonggang Lu, Peter H Westfall

{"title":"Comparing Objective and Subjective Bayes Factors for the Two-Sample Comparison: The Classification Theorem in Action.","authors":"Mithat Gönen, Wesley O Johnson, Yonggang Lu, Peter H Westfall","doi":"10.1080/00031305.2017.1322142","DOIUrl":"10.1080/00031305.2017.1322142","url":null,"abstract":"Many Bayes factors have been proposed for comparing population means in two-sample (independent samples) studies. Recently, Wang and Liu (2015) presented an \"objective\" Bayes factor (BF) as an alternative to a \"subjective\" one presented by Gönen et al. (2005). Their report was evidently intended to show the superiority of their BF based on \"undesirable behavior\" of the latter. A wonderful aspect of Bayesian models is that they provide an opportunity to \"lay all cards on the table.\" What distinguishes the various BFs in the two-sample problem is the choice of priors (cards) for the model parameters. This article discusses desiderata of BFs that have been proposed, and proposes a new criterion to compare BFs, no matter whether subjectively or objectively determined: A BF may be preferred if it correctly classifies the data as coming from the correct model most often. The criterion is based on a famous result in classification theory to minimize the total probability of misclassification. This criterion is objective, easily verified by simulation, shows clearly the effects (positive or negative) of assuming particular priors, provides new insights into the appropriateness of BFs in general, and provides a new answer to the question, \"Which BF is best?\"","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"73 1","pages":"22-31"},"PeriodicalIF":1.8,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6424525/pdf/nihms-1502428.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37086310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint clustering with correlated variables. 关联变量联合聚类。

IF 1.8 4区数学

American Statistician Pub Date : 2019-01-01 Epub Date: 2018-07-09 DOI: 10.1080/00031305.2018.1424033

Hongmei Zhang, Yubo Zou, Will Terry, Wilfried Karmaus, Hasan Arshad

{"title":"Joint clustering with correlated variables.","authors":"Hongmei Zhang, Yubo Zou, Will Terry, Wilfried Karmaus, Hasan Arshad","doi":"10.1080/00031305.2018.1424033","DOIUrl":"https://doi.org/10.1080/00031305.2018.1424033","url":null,"abstract":"Traditional clustering methods focus on grouping subjects or (dependent) variables assuming independence between the variables. Clusters formed through these approaches can potentially lack homogeneity. This article proposes a joint clustering method by which both variables and subjects are clustered. In each joint cluster (in general composed of a subset of variables and a subset of subjects), there exists a unique association between dependent variables and covariates of interest. To this end, a Bayesian method is designed, in which a semi-parametric model is used to evaluate any unknown relationships between possibly correlated variables and covariates of interest, and a Dirichlet process is utilized to cluster subjects. Compared to existing clustering techniques, the major novelty of the method exists in its ability to improve the homogeneity of clusters, along with the ability to take the correlations between variables into account. Via simulations, we examine the performance and efficiency of the proposed method. Applying the method to cluster allergens and subjects based on the association of wheal size in reaction to allergens with age, we found that a certain pattern of allergic sensitization to a set of allergens has a potential to reduce the occurrence of asthma.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"73 3","pages":"296-306"},"PeriodicalIF":1.8,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/00031305.2018.1424033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38325031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Letter to the Editor. 给编辑的信。

IF 1.8 4区数学

American Statistician Pub Date : 2019-01-01 Epub Date: 2019-08-05 DOI: 10.1080/00031305.2018.1537894

Thaddeus Tarpey, Eva Petkova

引用次数: 0