淡江大學機構典藏:Item 987654321/20723
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 57042/90725 (63%)
造访人次 : 12440733      在线人数 : 66
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/20723


    题名: Fitting logistic regression models with contaminated case–control data
    作者: Cheng, K. F.;Chen, Li-ching
    贡献者: 淡江大學統計學系
    关键词: Case–control data;Contamination;Logistic regression;Maximum likelihood;Misclassification
    日期: 2006-12-01
    上传时间: 2009-11-30 12:57:46 (UTC+8)
    出版者: Amsterdam: Elsevier BV * North-Holland
    摘要: Errors in measurement frequently occur in observing responses. If case–control data are based on certain reported responses, which may not be the true responses, then we have contaminated case–control data. In this paper, we first show that the ordinary logistic regression analysis based on contaminated case–control data can lead to very serious biased conclusions. This can be concluded from the results of a theoretical argument, one example, and two simulation studies. We next derive the semiparametric maximum likelihood estimate (MLE) of the risk parameter of a logistic regression model when there is a validation subsample. The asymptotic normality of the semiparametric MLE will be shown along with consistent estimate of asymptotic variance. Our example and two simulation studies show these estimates to have reasonable performance under finite sample situations.
    關聯: Journal of Statistical Planning and Inference 136(12), pp.4147-4160
    DOI: 10.1016/j.jspi.2005.07.009
    显示于类别:[統計學系暨研究所] 期刊論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    Fitting logistic regression models with contaminated case–control data.pdf165KbAdobe PDF0检视/开启
    index.html0KbHTML144检视/开启

    在機構典藏中所有的数据项都受到原著作权保护.

    TAIR相关文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈