
自引率: 7%
被引量: 21788
通过率: 暂无数据
审稿周期: 暂无数据
版面费用: 暂无数据
国人发稿量: 41
投稿须知/期刊简介:
Biometrics is published quarterly. Its general objects are to promote and extend the use of mathematical and statistical methods in various subject-matter disciplines, by describing and exemplifying developments in these methods and their application in a form readily assimilable by experimenters and those concerned primarily with analysis of data. The journal is a ready medium for publication of papers by both the experimentalist and the statistician. The papers in the journal include statistical, authoritative expository or review articles, and analytical or methodological papers contributing to the planning or analysis of experiments and surveys, or the interpretation of data. Many of the papers in Biometrics contain worked examples of the statistical analyses proposed.
期刊描述简介:
Published on behalf of the International Biometric Society, Biometrics emphasizes the role of statistics and mathematics in the biosciences. Its objectives are to promote and extend the use of statistical and mathematical methods in the principal disciplines of biosciences by reporting on the development and application of these methods. A centerpiece of most Biometrics articles is scientific application that sets scientific or policy objectives, motivates methods development, and demonstrates the operations of new methods. Papers in the journal appear in one of four sections. The Biometric Methodology section presents papers that focus on the development of new methods and results of use in the biosciences. The Biometric Practice section contains papers involving innovative applications of methods and providing practical contributions and guidance, biological insight, and/or significant new findings. Reader Reaction papers refer directly to articles previously published in the journal, and Letters to the Editors provide comments and suggestions on the journal and its content.
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Planning cost-effective operational forest inventories.
被引量:- 发表:2024
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Causal inference using multivariate generalized linear mixed-effects models.
被引量:- 发表:2024
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Leveraging independence in high-dimensional mixed linear regression.
被引量:- 发表:2024
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A Bayesian nonparametric approach for causal mediation with a post-treatment confounder.
被引量:- 发表:2024
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Designing cancer screening trials for reduction in late-stage cancer incidence.
被引量:- 发表:2024