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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/27380

    Title: The Fuzzy-weighted Quasi-Likelihood Estimation
    Authors: 吳忠武;Wu, Jong-wuu;蔡宗儒;Tsai, Tzong-ru
    Contributors: 淡江大學統計學系
    Keywords: Optimal fuzzy clustering method;Dimension reduction technique;Ordinary least squares estimator;Monte carlo simulation
    Date: 2000-03
    Issue Date: 2009-12-30 14:59:21 (UTC+8)
    Publisher: 淡江大學
    Abstract: When outliers infect data, the quasi-likelihood estimation will be affected and the fitted results will be inadequate. In this paper, we propose a fuzzy-weighted estimation that can reduce the influence of outliers efficiently. These fuzzy weights are computed by using the optimal fuzzy clustering method. Moreover, we call this new estimation procedure as fuzzy-weighted quasi-likelihood estimation. Under some weak conditions, we show the fuzzy-weighted estimators are √n-consistent and asymptotically normal. Moreover, a practical example and some Monte Carlo simulations are used to demonstrate the application of the fuzzy-weighted estimation method.
    Relation: International Journal of Information and Management Sciences 11(1), pp.65-73
    Appears in Collections:[Graduate Institute & Department of Statistics] Journal Article

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