博碩士論文 92245003 完整後設資料紀錄

DC 欄位 語言
DC.contributor統計研究所zh_TW
DC.creator沈仲維zh_TW
DC.creatorChung-Wei Shenen_US
dc.date.accessioned2009-6-10T07:39:07Z
dc.date.available2009-6-10T07:39:07Z
dc.date.issued2009
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=92245003
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.abstract本論文首先介紹Royall與Tsou在2003年所提出之強韌概似函數的方法。其次,將其方法與觀念,應用於分析相關性的有序(ordinal)資料並於資料平均數在廣義線性模型的架構下,推導大樣本時,有興趣之迴歸參數的強韌概似函數。最後,將平均數由廣義線性模型進ㄧ步推廣到部分線性模型的架構且同樣推導大樣本時,有興趣之迴歸參數的強韌概似函數。 值得注意的是這些概似函數並不需要知道資料的真實分配,只需要假設二階或四階動差存在即可。最後,利用模擬與真實資料的分析來呈現此強韌方法的效率。 zh_TW
dc.description.abstractIn this thesis, we first introduce the idea of robust likelihood functions proposed by Royall and Tsou (2003). Next, we provide a parametric robust method originated from this idea to make inferences for correlated ordinal data and develop the robust likelihood functions for regression coefficients of mean modeled in a generalized linear model fashion. Finally, we extend the robust likelihood technique from generalized linear models (GLM) to partially-linear models (PLM), and use normal distribution as the working model to develop the robust likelihood functions for regression coefficients in large samples. The legitimacy of this novel approach requires no knowledge of the underlying joint distributions so long as their second or fourth moments exist. The efficacy of the proposed parametric approach is demonstrated via simulations and the analyses of several real data sets. en_US
DC.subject部份線性模型zh_TW
DC.subject廣義線性模型zh_TW
DC.subject強韌概似函數zh_TW
DC.subject相關性的有序資料zh_TW
DC.subjectPartially linear modelsen_US
DC.subjectGeneralized Linear modelsen_US
DC.subjectRobust likelihood functionen_US
DC.subjectCorrelated ordinal dataen_US
DC.title強韌概似函數更廣泛之應用zh_TW
dc.language.isozh-TWzh-TW
DC.titleMore on the Applicability of the Robust Likelihood Methodologyen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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