博碩士論文 88245002 完整後設資料紀錄

DC 欄位 語言
DC.contributor統計研究所zh_TW
DC.creator簡莉珠zh_TW
DC.creatorLi-Chu Chienen_US
dc.date.accessioned2005-6-22T07:39:07Z
dc.date.available2005-6-22T07:39:07Z
dc.date.issued2005
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=88245002
dc.contributor.department統計研究所zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstractIn 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.en_US
DC.subjectANCOVAen_US
DC.subjectnormal regressionen_US
DC.subjectgamma regressionen_US
DC.subjectinverse Gaussian regressionen_US
DC.subjectANOVAen_US
DC.subjectGeneralized linear modelsen_US
DC.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zh_TW
dc.language.isozh-TWzh-TW
DC.titleParametric Simultaneous Robust Inferences for Regression Coefficients in General Regression Problems under Generalized Linear Modelsen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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