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    題名: Adjusted Jackknife Estimation Method in Quasi-Likelihood Model with Outliers
    作者: Tsai, Tzong-ru;Wu, Shuo-jye
    貢獻者: 淡江大學統計學系
    關鍵詞: Asymptotic Normality;Fuzzy-Weighted Estimation;Link Function;Optimal Fuzzy Clustering Method;Semi-Parametric Model
    日期: 2001-09
    上傳時間: 2009-12-30 14:59:30 (UTC+8)
    出版者: 淡江大學
    摘要: 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.
    關聯: International Journal of Information and Management Sciences 12(3), pp.57-69
    DOI: 
    顯示於類別:[統計學系暨研究所] 期刊論文

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