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上传于：2016-03-21

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    188宝金博页面版: EXTERNAL SCIENTIFIC REPORT - efsa.europa.eu

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    内容提示： EXTERNAL SCIENTIFIC REPORT APPROVED: 23.09.2015 PUBLISHED: 30.09.2015 www.efsa.europa.eu/publications EFSA Supporting publication 2015:EN-869 Separation Issues and Possible Solutions: Part I – Systematic Literature Review on Logistic Models Ensoy C, Rakhmawati TW, Faes C, Aerts M CenStat, Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BIOSTAT) Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium Abstract The separation issue is a complication frequently ...

    文档格式：PDF | 页数：40 | 浏览次数：81 | 上传日期：2016-03-21 22:50:01 | 文档星级：
    EXTERNAL SCIENTIFIC REPORT APPROVED: 23.09.2015 PUBLISHED: 30.09.2015 www.efsa.europa.eu/publications EFSA Supporting publication 2015:EN-869 Separation Issues and Possible Solutions: Part I – Systematic Literature Review on Logistic Models Ensoy C, Rakhmawati TW, Faes C, Aerts M CenStat, Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BIOSTAT) Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium Abstract The separation issue is a complication frequently occurring when sparse data are analysed by logistic regression models. Such analyses of sparse datasets tend to be biased, resulting in misleading conclusions, or may even not be feasible, due to computational problems such as non-convergence. In this report, a systematic literature review (SLR) was carried out to describe the phenomenon as well as the methods that deal with the separation issue in binary response models, especially in logistic regression type of models. Methods dealing with separation are categorized regarding the statistical paradigm and framework in which they are proposed and studied: Likelihood, Bayesian, GEE and cluster specific/random effects approach. © Ensoy C, Rakhmawati TW, Faes C, Aerts M, 2015 Key words: Logistic Regression, Binary Model, Separation Issue, Exact method, Firth method. Question number: EFSA-Q-2014-00123 Correspondence: amu@efsa.europa.eu

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