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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/7478

    Title: 廣義線性模型架構下多個迴歸係數的有母數強韌推論法;Parametric Simultaneous Robust Inferences for Regression Coefficients in General Regression Problems under Generalized Linear Models
    Authors: 簡莉珠;Li-Chu Chien
    Contributors: 統計研究所
    Keywords: ANCOVA;normal regression;gamma regression;inverse Gaussian regression;ANOVA;Generalized linear models
    Date: 2005-05-26
    Issue Date: 2009-09-22 10:58:34 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: none In this article, robust regression parameter inference in the setting of generalized linear models (GLMs) will be proposed. A parametric robust regression methodology that is robust to violations of the distributional assumptions is able to test hypotheses on the regression coefficients in the misspecified GLM setting. More specifically, it will be demonstrated that with large samples the ordinary normal, gamma and inverse Gaussian regression models can be made robust and provide consistent regression parameter estimates in the misspecified GLM setting. These adjusted regression models furnish the correct type I, II error probabilities, and also the correct coverage probability, for continuous data, as long as the true but unknown underlying distributions have finite second moments. The parametric robust regression techniques are also applied to the analysis of variance (ANOVA) problems including the one-way, two-way ANOVA structures and the one-way analysis of covariance (ANCOVA) setup. In the ANOVA situations, these adjusted regression models continue to remain asymptotically valid representations of the particular parameters of interest, whatever distributions generate the data.
    Appears in Collections:[統計研究所] 博碩士論文

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