博碩士論文 105225005 完整後設資料紀錄

DC 欄位 語言
DC.contributor統計研究所zh_TW
DC.creator何建興zh_TW
DC.creatorJian-Xing Heen_US
dc.date.accessioned2018-6-29T07:39:07Z
dc.date.available2018-6-29T07:39:07Z
dc.date.issued2018
dc.identifier.urihttp://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=105225005
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.abstractCopula 是目前分析相關性資料時,常用的一種建構聯合分配函數的一種方法,其 優點在於可以便利地建構所需的聯合分配函數,但當Copula 模型假設錯誤時,其統計 推論的正確性卻鮮有人探討。本文主要的目的是在廣義線性模型下,探討當Copula 模 型假設錯誤時,其迴歸參數之估計量是否具有一致性,且能否提供正確的統計推論, 並與Gamma-Gamma 模型、Poisson-Negative Binomial 模型以及Bivariate Negative Binomial 模型之結果做比對。zh_TW
dc.description.abstractCopulas are popular and commonly used methods for constructing joint distribution functions when analyzing correlated data. The advantage of Copulas is that one can easily construct joint distribution functions with desired marginals. However, the validity of inference based on Copulas under model misspecification is rarely investigated. The objective of this paper is to examine the properties of the Copula-based estimates of the regression parameters given that the assumptions of the Copula model fail. We also make comparisons between several Copula models with other methods for analyzing bivariate correlated data.en_US
DC.subject相關性資料zh_TW
DC.subject強韌概似函數zh_TW
DC.subjectCopulazh_TW
DC.subjectcorrelated dataen_US
DC.subjectrobust likelihood functionen_US
DC.subjectcopulaen_US
DC.title在模型錯誤下copula應用在迴歸分析時的正確性zh_TW
dc.language.isozh-TWzh-TW
DC.titleThe validity of copula for regression when model assumptions failen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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