淡江大學機構典藏:Item 987654321/20601
English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 64188/96968 (66%)
造訪人次 : 11333630      線上人數 : 321
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧：
  • 您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料
    •  進階搜尋


    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/20601


    題名: Joint conditional likelihood estimator in logistic regression with missing covariate data
    作者: Wang, C. Y.;Chen, J. C.;Lee, S. M.;Ou, S. T.
    貢獻者: 淡江大學統計學系
    關鍵詞: Estimators;Maximum likelihood estimation;Estimation bias;Consistent estimators;Statistical estimation;Logistic regression;Estimators for the mean;Cigarette smoking;Analytical estimating;Correlation coefficients
    日期: 2002-04
    上傳時間: 2013-08-08 14:48:23 (UTC+8)
    出版者: Taipei: Academia Sinica * Institute of Statistical Science
    摘要: This article considers semiparametric estimation in logistic regression with missing covariates. In a validation subsample, we assume covariates are measured without error. Some covariates are missing in the non-validation set, while surrogate variables may be available for all study subjects. We consider the case when a covariate variable is missing at random such that the selection probability of the validation set depends only on observed data. Breslow and Cain (1988) proposed a conditional likelihood approach based on the validation set. We combine the conditional likelihoods of the validation set and the non-validation set. The proposed estimator is easy to implement and is semiparametric since no additional model assumption is imposed. Large sample theory is developed. For the estimation of the parameter for the missing covariate, simulations show that, under various situations, the proposed estimator is significantly more efficient than the validation likelihood estimator of Breslow and Cain and the inverse selection probability weighted estimator. Under moderate sample sizes and moderate values of relative risk parameters, our estimator remains competitive when compared with the nonparametric maximum likelihood estimator of Scott and Wild (1997). The proposed method is illustrated by a real data example.
    關聯: Statistica Sinica 12(2), pp.555-574
    顯示於類別:[統計學系暨研究所] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html0KbHTML325檢視/開啟
    index.html0KbHTML157檢視/開啟
    Joint conditional likelihood estimator in logistic regression with missing covariate data.pdf219KbAdobe PDF1檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

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