Correcting bias due to misclassification in the estimation of logistic regression models

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/27785

Title:	Correcting bias due to misclassification in the estimation of logistic regression models
Authors:	Cheng,KF;Hsueh,HM
Contributors:	統計研究所
Keywords:	DOUBLE SAMPLING SCHEME;MEASUREMENT ERROR;BINOMIAL DATA
Date:	1999
Issue Date:	2010-06-29 19:33:34 (UTC+8)
Publisher:	中央大學
Abstract:	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. (C) 1999 Elsevier Science B.V. All rights reserved.
Relation:	STATISTICS & PROBABILITY LETTERS
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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