Statistical science : a review journal of the Institute of Mathematical Statistics最新文献

Missing Information Principle: A Unified Approach for General Truncated and Censored Survival Data Problems. 缺失信息原则:一般截断和删节生存数据问题的统一方法。

Statistical science : a review journal of the Institute of Mathematical Statistics Pub Date : 2018-05-01 DOI: 10.1214/17-STS638

Yifei Sun, J. Qin, Chiung-Yu Huang

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引用次数: 2

Scalable Genomics with R and Bioconductor 可扩展基因组学与R和Bioconductor

Statistical science : a review journal of the Institute of Mathematical Statistics Pub Date : 2014-05-01 DOI: 10.1214/14-STS476

Michael F. Lawrence, Martin T. Morgan

引用次数: 9

Discussion of "Estimating Random Effects via Adjustment for Density Maximization" by C. Morris and R. Tang. C. Morris和R. Tang对“通过密度最大化调整估计随机效应”的讨论。

Statistical science : a review journal of the Institute of Mathematical Statistics Pub Date : 2011-08-19 DOI: 10.1214/11-STS349B

P. Lahiri, S. Pramanik

引用次数: 7

Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data. 评论:揭开双重稳健性的神秘面纱:从不完整数据估计总体均值的不同策略的比较。

Statistical science : a review journal of the Institute of Mathematical Statistics Pub Date : 2008-04-18 DOI: 10.1214/07-STS227B

A. Tsiatis, M. Davidian

{"title":"Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data.","authors":"A. Tsiatis, M. Davidian","doi":"10.1214/07-STS227B","DOIUrl":"https://doi.org/10.1214/07-STS227B","url":null,"abstract":"We congratulate Drs. Kang and Schafer (KS henceforth) for a careful and thought-provoking contribution to the literature regarding the so-called “double robustness” property, a topic that still engenders some confusion and disagreement. The authors’ approach of focusing on the simplest situation of estimation of the population mean μ of a response y when y is not observed on all subjects according to a missing at random (MAR) mechanism (equivalently, estimation of the mean of a potential outcome in a causal model under the assumption of no unmeasured confounders) is commendable, as the fundamental issues can be explored without the distractions of the messier notation and considerations required in more complicated settings. Indeed, as the article demonstrates, this simple setting is sufficient to highlight a number of key points. \u0000 \u0000As noted eloquently by Molenberghs (2005), in regard to how such missing data/causal inference problems are best addressed, two “schools” may be identified: the “likelihood-oriented” school and the “weighting-based” school. As we have emphasized previously (Davidian, Tsiatis and Leon, 2005), we prefer to view inference from the vantage point of semi-parametric theory, focusing on the assumptions embedded in the statistical models leading to different “types” of estimators (i.e., “likelihood-oriented” or “weighting-based”) rather than on the forms of the estimators themselves. In this discussion, we hope to complement the presentation of the authors by elaborating on this point of view. \u0000 \u0000Throughout, we use the same notation as in the paper.","PeriodicalId":397321,"journal":{"name":"Statistical science : a review journal of the Institute of Mathematical Statistics","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128037706","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: 502

Ethics and statistics in randomized clinical trials. 随机临床试验中的伦理学和统计学。

Statistical science : a review journal of the Institute of Mathematical Statistics Pub Date : 1900-01-01 DOI: 10.1214/SS/1177011934

R. Royall, R. Bartlett, R. G. Cornell, D. Byar, W. Dupont, R. Levine, Foster Lindley, R. Simes, M. Zelen

引用次数: 128