Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/17756
標題: 具多變量t分佈與AR(p)相依的線性混合效應模型之長期資料分析
Longitudinal Data Analysis Using Multivariate t Linear Mixed Models With AR(p) Dependence
作者: 藍啟文
Lan, Chi-Wen
關鍵字: conditional prediction
條件預測
hybrid algorithm
outliers
random effects
reparameterization
混合演算法
離群值
隨機效應
重新參數化
出版社: 應用數學系所
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摘要: 本文提出具多變量t分佈與AR(p)相依結構之混合效應模式來配適具厚尾特徵並且有自相關之長期資料。 對於參數最大概似估計值之計算方法, 我們發展一種用ECME演算法尋求較佳起始值之混合 ECME-scoring 的演算法來加快收斂速度。 此外,我們也考慮用經驗貝式法去計算隨機效應的估計值並且也提出未來值的預測方法。 我們將所提出的方法應用在一組22隻裸鼠的異種皮移植腫瘤實驗之真實資料上, 由數據上的比較可以得知無論是在參數估計或是未來值的預測, 本文中所提出來的模式所得之結果皆較以多變量常態為基礎之模式較佳。
The t linear mixed model with AR(p) dependence is proposed for longitudinal studies when data contain both thick tails and serial correlations. For computational purposes, we implement a hybrid maximization approach starting with a few ECME steps and finishing with scoring steps to enhance the convergence speed. Empirical Bayes estimation of random effects and prediction of future values for the proposed model are also considered. The methodology is applied to a real example for an experiment of tumor growth on twenty-two mice. Numerical comparisons indicate that the proposed model outperforms the normal model from both inferential and predictive perspectives.
URI: http://hdl.handle.net/11455/17756
其他識別: U0005-0908200715403400
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0908200715403400
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