博碩士論文 93225024 完整後設資料紀錄

DC 欄位 語言
DC.contributor統計研究所zh_TW
DC.creator王志偉zh_TW
DC.creatorChih-Wei Wangen_US
dc.date.accessioned2007-1-25T07:39:07Z
dc.date.available2007-1-25T07:39:07Z
dc.date.issued2007
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=93225024
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隨機效應混合模型是時常被用來建構長時期追蹤資料的一類普遍模型。在實驗對象之中,這些模型的隨機效應共變異矩陣典型地被假設為常數。這篇論文中,我們採用一種特殊的Cholesky矩陣分解法去建構隨機效應共變異矩陣而且允許這種分解中所引進的參數是依賴實驗對象特性共變數。一種跟隨著Metropolis-Hastings步驟的Gibbs抽樣方法在這裡被實行用來幫助我們作出貝氏推論。此外,對於每個實驗對象,根據先前已觀測到的資料去預測未來的觀測資料是我們的另一個主題。一些模擬上的研究將被實行用來驗證我們的方法論以及常態分配測量誤差模型與學生t分配測量誤差模型在這裡將被比較。zh_TW
dc.description.abstractRandom effects (mixed) models are a common class of models used frequently to model longitudinal data. The random effects covariance matrix of these models is typically assumed constant across subject. In this thesis, we use a special Cholesky decomposition of the matrix to model the random effects covariance matrix and allow the parameters that result from this decomposition to depend on subject-specific covariates. A simple Gibbs sampler together with Metropolis-Hastings (M-H) steps can be implemented here to draw the Bayesian inference. Furthermore, predicting the future observations given the previous observed data for each subject is our another topic. Several simulation studies are carried out to demonstrate our methodologies and comparisons are make from both normal and t measurement error models.en_US
DC.subjectBayesian inferenceen_US
DC.subjectCholesky decompositionen_US
DC.subjectRandom effectsen_US
DC.subjectMixed modelen_US
DC.subjectMarkov chain Monte Carloen_US
DC.subjectPredictionen_US
DC.titleBayesian Prediction on Longitudinal Data with Random Effects Covariance Matrixen_US
dc.language.isoen_USen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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