English  |  正體中文  |  简体中文  |  Items with full text/Total items : 49195/83607 (59%)
Visitors : 7093001      Online Users : 52
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU 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
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/27381


    Title: Adjusted Jackknife Estimation Method in Quasi-Likelihood Model with Outliers
    Authors: Tsai, Tzong-ru;Wu, Shuo-jye
    Contributors: 淡江大學統計學系
    Keywords: Asymptotic Normality;Fuzzy-Weighted Estimation;Link Function;Optimal Fuzzy Clustering Method;Semi-Parametric Model
    Date: 2001-09
    Issue Date: 2009-12-30 14:59:30 (UTC+8)
    Publisher: 淡江大學
    Abstract: Many statisticians usually use a quasi-likelihood model to examine the relationship between response variable and explanatory variables. In many applications, the data set often contains outliers and, hence the traditional estimation methods may not be adequate. In this paper, we develop an adjusted jackknife estimation method to solve this problem. The advantage of adjusted jackknife estimation method is that the influence of outliers in parameter estimation can be reduced efficiently. The asymptotic properties of the adjusted jackknife estimator are derived when the link function is linear. Some Monte Carlo simulations and one example are provided to demonstrate the application of the adjusted jackknife estimation method.
    Relation: International Journal of Information and Management Sciences 12(3), pp.57-69
    DOI: 
    Appears in Collections:[統計學系暨研究所] 期刊論文

    Files in This Item:

    File SizeFormat
    index.html0KbHTML36View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


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