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    題名: Correcting bias due to misclassification in the estimation of logistic regression models
    作者: Hsueh,Huey-Miin;Cheng, K. F.
    薛慧敏
    貢獻者: 統計系
    日期: 1999-09
    上傳時間: 2014-12-23 15:09:01 (UTC+8)
    摘要: This paper describes several properties of some bias correction methods in the estimation of logistic regression models with misclassification in the binary responses. The observation error model consists of a primary data set plus a smaller validation set. The large sample properties of different bias correction methods are compared under various situations, and the asymptotic relative efficiencies of some important methods are derived. Our small sample simulation studies conclude that the semiparametric estimation method considered by Pepe (Biometrika 79(1992)355–365) is quite reliable under a reasonable surrogate classifier.
    關聯: Statistics & Probability Letter,44(3), 229-240
    資料類型: article
    顯示於類別:[統計學系] 期刊論文

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