English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 109077/140081 (78%)
Visitors : 43392050      Online Users : 683
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 理學院 > 應用數學系 > 期刊論文 >  Item 140.119/120184
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/120184

    Title: Estimation of a logistic regression model with mismeasured observations
    Authors: Cheng, K. F.
    Hsueh, H. M.
    Contributors: 應數系
    Keywords: Kernel estimation;estimated likelihood;logistic regression;measurement error;misclassification
    Date: 2003-01
    Issue Date: 2018-09-27 17:21:41 (UTC+8)
    Abstract: We consider the estimation problem of a logistic regression model. We assume the response observations and covariate values are both subject to measurement errors. We discuss some parametric and semiparametric estimation methods using mismeasured observations with validation data and derive their asypmtotic distributions. Our results are extentions of some well known results in the literature. Comparisons of the asymptotic covariance matrices of the studied estimators are made, and some lower and upper bounds for the asymptotic relative efficiencies are given to show the advantages of the semiparametric method. Some simulation results also show the method performs well.
    Relation: Statistica Sinica , Vol. 13, No. 1 (January 2003), pp. 111-127
    Data Type: article
    Appears in Collections:[應用數學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    A13n17.pdf434KbAdobe PDF2190View/Open

    All items in 政大典藏 are protected by copyright, with all rights reserved.

    社群 sharing

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback